A Method for Obtaining 3D Point Cloud Data by Combining 2D Image Segmentation and Depth Information of Pigs

Abstract

Simple Summary

This paper presents a technique for acquiring 3D point cloud data of pigs in precision animal husbandry. The method combines 2D detection frames and segmented region masks of pig images with depth information to improve the efficiency of acquiring 3D data. Our method achieves an average similarity of 95.3% compared to manually labelled 3D point cloud data. This method provides technical support for pig management, welfare assessment, and accurate weight estimation.

Abstract

This paper proposes a method for automatic pig detection and segmentation using RGB-D data for precision livestock farming. The proposed method combines the enhanced YOLOv5s model with the Res2Net bottleneck structure, resulting in improved fine-grained feature extraction and ultimately enhancing the precision of pig detection and segmentation in 2D images. Additionally, the method facilitates the acquisition of 3D point cloud data of pigs in a simpler and more efficient way by using the pig mask obtained in 2D detection and segmentation and combining it with depth information. To evaluate the effectiveness of the proposed method, two datasets were constructed. The first dataset consists of 5400 images captured in various pig pens under diverse lighting conditions, while the second dataset was obtained from the UK. The experimental results demonstrated that the improved YOLOv5s_Res2Net achieved a [email protected]:0.95 of 89.6% and 84.8% for both pig detection and segmentation tasks on our dataset, while achieving a [email protected]:0.95 of 93.4% and 89.4% on the Edinburgh pig behaviour dataset. This approach provides valuable insights for improving pig management, conducting welfare assessments, and estimating weight accurately.

1. Introduction

Animal and livestock products are vital for human survival. However, the safety of the world’s food supply is endangered by factors such as coronaviruses, climate change, and dietary habits. As a result, the number of people experiencing global food shortages has increased significantly [1,2]. On the other hand, with the rise in living standards, people’s dietary structures are also changing. The level of consumption of meat and other protein-rich foods with integrity and high quality is rising [3,4]. According to data from the OECD-FAO Agricultural Outlook, pork is the most-produced livestock product in the world, accounting for nearly one-third of all meat consumption [5].

In recent decades, precision livestock farming technology has been widely used to promote normal pig production yield, improve farming production, save farming expenses, reduce feed wastage and environmental pollution, ensure food quality and safety, and benefit human health [6]. Monitoring the health status of pigs can help managers control the spread of diseases in time to protect healthy pigs [7]. Automatic or intelligent pig identification and monitoring are necessary for intensive pig farming and play a significant role in increasing the effectiveness of pig management and feeding [8]. Pig detection and segmentation, in particular, are the foundation for pig monitoring and continuously gathering data on pig welfare and health [9]. A reliable detection and segmentation technique can increase the accuracy of pig monitoring [10].

A variety of deep learning techniques for pig detection have been implemented as a result of the growth of convolutional neural networks (CNNs) [11,12]. Using Faster R-CNN, Yang et al. were able to localise pigs from cluster housing with an accuracy of 96.58% [13]. In 2020, Riekert et al. used the neural architecture search base network to improve Faster R-CNN’s backbone for extracting features from 2D images of pigs, resulting in an accuracy of 80.20% for model detection [14]. In 2021, Yin et al. improved YOLOv3 by adding an attention mechanism to complete real-time pig detection and achieved 94.12% detection accuracy [15]. Ahn et al. fused the YOLOv4 model that was trained with the original data and the YOLOv4 model that was trained with the enhanced data, which improved the detection performance of the model to 94.33% [16]. That same year, Wutke et al. established a CNN algorithm that followed the autoencoder structure to achieve 95.4% accuracy in pig detection [17]. In 2022, To tackle image noise triggered by factors such as flying insects, Bo et al. used a generative adversarial model to remove noise from images. Removing noise increased the algorithm’s accuracy from 76.6% to 90.6% [18]. Kim et al. achieved 99.44% detection accuracy and 30 frames per second real-time detection speed by embedding the lighter YOLOv4 model into an embedded circuit board [19]. Xiao et al. designed a cascaded Faster R-CNN pig detector to achieve 98.4% pig detection accuracy [20]. Highly accurate and efficient detection algorithms play an important role in smart farming.

In recent years, pig segmentation technology has also made considerable progress, and many researchers have proposed different methods to improve the accuracy and efficiency of segmentation. For example, In 2021, Tu et al. used Mask Scoring R-CNN (MS R-CNN) and the Soft NMS algorithm to achieve a new instance segmentation method [21]. Hu et al. proposed a new dual attention block and introduced it into the Feature Pyramid Network (FPN) to achieve instance segmentation performance with an average precision of 93.1% [22]. Zhai et al. used an improved Mask R-CNN to achieve effective segmentation of sticky pigs, with a segmentation accuracy of 91.7% [23]. In addition, in 2022, Liu et al. integrated DeepLab V3+, a model with an attention mechanism, and a model for high-frequency and low-frequency feature fusion to build an integrated segmentation model, achieving 76.31% MIoU [24]. Lu et al. used Swin Transformer method to achieve pig segmentation accuracy of 86.9% [25]. Gan et al. used an anchor-free network and a graph convolutional network to achieve piglet segmentation accuracy of 95.2% [26]. Zhong et al., based on living visible light and thermal infrared images, proposed a multi-source fusion algorithm, achieving a high-precision segmentation of pigs [27]. These methods have made considerable progress in addressing challenges such as complex backgrounds, variations in lighting conditions, and occlusion in pig segmentation, thereby offering valuable technical assistance for intelligent pig management and animal welfare assessment.

The 3D method is an extension of the 2D method and can solve some problems more effectively. Compared to the limitations of 2D methods in complex spatial environments and tasks, 3D methods can extract rich geometric and spatial information from three-dimensional data, providing more effective scale and location information for objects and more easily handling issues such as occlusion [28,29]. Based on deep learning, point cloud detection and segmentation tasks can be divided into two categories: indirect and direct point cloud segmentation [30,31]. Indirect point cloud detection and segmentation utilise CNN technology to process structured feature point cloud data, which requires preprocessing of point cloud data through gridding and voxelization. Many works use multiple-view 2D image reconstruction of 3D views [32,33,34] or 3D CNNs to achieve 3D detection and semantic segmentation tasks [35,36,37,38,39]. However, this method not only requires complex data preprocessing operations but also loses a large amount of point cloud information [30].

Direct point cloud segmentation abandons the complicated prior settings of point cloud data and directly utilises deep learning techniques for end-to-end learning of target point cloud features. In 2017, Qi et al. first proposed a point-level prediction network called PointNet, which can learn translation and rotation invariant information of unstructured point clouds [40]. Subsequent studies have focused on enhancing local region feature learning [41,42], graph-based point cloud neighborhood feature learning [43,44,45,46,47], and point cloud attention feature learning [30,48,49,50,51,52,53].

Three-dimensional point cloud detection and instance segmentation of pigs is a research direction with important application value. However, there are few studies on the detection and segmentation of pig 3D point clouds, and most focus on the estimation of pig weight. Some studies use devices such as Kinect V3 [54,55], Realsense [56,57,58], and a laser scanner [59] to collect point cloud data of single pigs, and remove background point clouds by setting the background depth to 0 and using the subtraction programme, achieving instance segmentation of pigs. In addition, Ref. [60] utilised an instance 2D segmentation network and depth image to perform accurate 3D point cloud segmentation for individual pigs, but they did not assess the effectiveness of the network in segmenting multiple pig 3D point clouds in complex environments. However, most of the existing methods are based on depth cameras to acquire point cloud data of individual pigs, and they utilise subtraction algorithms to remove ground point clouds for 3D detection and segmentation of single pigs [54,55,56,57,58,59]. These approaches not only encounter difficulties in removing extraneous object point clouds around the cleaning device but also struggle with the instance segmentation of multiple cleaning devices’ 3D point clouds to achieve an automatic estimation of the weights of all pigs in the pigsty.

Considering the challenges in 3D detection and segmentation to acquire 3D pig body information, we propose a novel approach that combines 2D detection and segmentation with depth information to enable easy and fast acquisition of 3D pig body point cloud information. The paper introduces a Res2Net block into YOLOv5 to enhance the model’s fine-grained feature extraction and improve its detection and segmentation performance. Additionally, the paper proposes a 3D point cloud detection and segmentation algorithm that uses depth data and generated detection and segmentation masks to achieve a simple and efficient 3D point cloud multi-pig detection and segmentation method.

This paper is structured as follows: Section 2 describes the dataset acquisition devices and schemes, as well as the improvement of the method. Section 3 introduces the experimental setup. Section 4 illustrates and discusses the experimental results of pig detection and segmentation. Section 5 discusses the advantages, limitations, and generalization of our approach. Finally, conclusions and recommendations for additional research are provided in Section 6.

2. Materials and Methods

2.1. Data Acquisition and Preprocessing

To validate the effectiveness of our approach in complex environments, we have constructed two datasets with complex environments (variations in lighting, etc.): our dataset and the Edinburgh (UK) pig behaviour dataset.

From 22 November 2021 to 10 January 2022, experimental data were gathered on a farm in Fanzhuang Village, Dongping County, Tai’an City, Shandong Province, China. On this farm, more than 150 New America Ternary pigs were bred in 16 pigsties. Each pigsty measured approximately 5 m in length and 2.7 m in width and was equipped with a drinking nipple and a feeding trough. Typically, each pen housed 7–10 pigs that were fed three times a day (morning, noon, and afternoon). Figure 1 depicts the entire data collection platform. Intel RealSense D455 depth cameras (Intel Corporation, Santa Clara, CA, USA) were mounted on a horizontal post 3 m high in the centre of each of four pigsties to capture top-view images of the entire pigsty. The resolution of the data collected by the D455 depth camera was set to 848 × 480 with an FPS of 15 and all other parameter configurations were set to their default values. The D455 used a 10-m Type-C connection to transmit the voluminous 3D point cloud data it gathered to the server.

Changes in illumination present a significant challenge for long-term pig monitoring. To evaluate the effectiveness of the proposed approach, data were collected three times during the day (09:00, 12:00, and 16:00) and three times in the evening (17:00, 17:20, and 17:40) over several months as the pigs grew. A total of 2 h of data were collected each day, resulting in a total of 5400 daytime and 5400 nighttime image datasets. A selection of these images can be observed in Figure 2, which shows data acquired at 09:00, 12:00, 16:00, 17:00, 17:20, and 17:30.

Although pig activity was more active during the data collection period, many redundant and similar images were still present in the collected data. To address this issue, 132,766 images in the PNG format were obtained by processing the original video stream data and extracting one image every 2 s. The double hash algorithm [61] was then used to identify and remove similar images, resulting in 5400 images after processing. We used a total of 6253 images from the Edinburgh dataset, as shown in Figure 3. The UK dataset is openly accessible at https://homepages.inf.ed.ac.uk/rbf/PIGDATA/ (accessed on 8–10 February 2021). To augment the training data, a mosaic data enhancement method was employed. This technique involves randomly cropping four images and stitching them together to create a new training image in Figure 4. In addition to enhancing the training data, this technique also increases batch size and improves the training effectiveness from different perspectives [62].

2.2. Fast 3D Detection of Pigs Based on YOLOv5s

Regular monitoring and recording of pig weight can provide valuable insights into their growth and development. Utilising 3D object detection technology to estimate the weight of pigs presents a superior approach compared to relying solely on 2D images. This technology can capture more comprehensive data on the body shape of pigs, resulting in more proficient and precise weight estimation. In this study, we implemented an enhanced version of the YOLOv5s model to facilitate efficient 3D pig detection and segmentation procedures using depth images, detection results, and semantic segmentation masks. We improved the YOLOv5s model’s performance in detection and segmentation by replacing its original bottleneck structure with a Res2Net bottleneck structure, which enhances the backbone network’s ability to extract fine-grained features. The fast 3D pig detection based on the improved YOLOv5s model structure is shown in Figure 5.

2.2.1. Improved Backbone with Res2Net

The backbone’s ability to extract image features is critical to the overall network model. Gradient dispersion and feature redundancy are issues that YOLOv5s employs bottleneck modules to address throughout the feature extraction process. This paper introduced the Res2Net bottleneck in place of YOLOv5s’s bottleneck to enhance the backbone’s capability to withdraw features [63].

The Res2Net bottleneck builds hierarchical residual class connections inside the residual structure to extract multiscale features at the granularity level. As shown in Figure 6, this module performs residual concatenation by dividing convolutional features into four groups (X1, X2, X3, X4) and extracting features for each group in turn (K2, K3, K4). Finally, the four groups are concatenated into one group (Y1, Y2, Y3, Y4) to achieve finer-grained multiscale feature representation.

2.2.2. Acquisition of 3D Information on Pigs

In pig feeding, the pig body size is important for describing pig growth and reproductive performance; the pig weight is important information for helping farmers optimise feed and management; and the pig body condition score is an important parameter for monitoring pig welfare and health. Compared with 2D vision technology, 3D vision technology is more advantageous for tasks such as body size measurement, body condition scoring, and weight prediction. Therefore, developing a fast and efficient multi-pig point cloud detection and segmentation technique is the key to achieving a real-time estimation of multi-pig weight.

To achieve these tasks, this study proposes a fast 3D pig detection and segmentation method based on 2D pig image detection and semantic segmentation. This method utilises the current mature 2D object detection and segmentation techniques, and can quickly complete 3D detection and segmentation without training, as shown in the pseudocode in Algorithm 1.    

Algorithm 1: Obtain the 3D point cloud coordinates of the detected object in the 2D image

[Figure omitted. See PDF]

In Algorithm 1, RGBImage represents an array for reading the RGB images, DepthImage represents an array for reading the deep images, ObjectMask represents an array for reading the detection and segmentation mask images, MaskColourList represents the mask colours corresponding to different detection objects, and ObjectPointCloudList represents a list of point clouds for different objects. OriginalPointCloud refers to the point cloud that is generated from the RGB image and depth image, while MaskPointCloud represents the mask point cloud generated by the detection and segmentation mask image and depth image. We obtain the instance segmentation mask of pigs by combining detection frame masks and semantic segmentation masks, and we use the RGB image and depth image to achieve 3D point cloud multi-pig instance detection and segmentation in Figure 7.

3. Experimental Setup

3.1. Experimental Platform

In our experiments, a computer running Windows 10 Professional Edition (64-bit) with an Intel${}^{\circledR }$ Core${}^{\mathrm{TM}}$ i7-11700 @ 2.50 GHz CPU (Intel Corporation) and NVIDIA GeForce GTX 1080Ti GPU (Nvidia Corporation, Santa Clara, CA, USA) was used (Table 1). The proposed method was programmed using the PyTorch v3.8 framework.

3.2. Experimental Dataset Construction

We utilised Labelme software [64] to perform detection and segmentation annotations on images of pigs (Figure 8), resulting in a dataset consisting of a total of 5400 images, with 2700 images for each category. One category contains images taken between 09:00 and 16:00, while the other category contains images taken between 17:00 and 18:00. For the dataset used in our experiments, 70% of the data were allocated to the training set and 30% to the testing set. In addition, we permuted the Edinburgh dataset in the same way to obtain a training set of 4377 images and a validation set of 1876 images.

3.3. Network Training Parameters

In this study, we set the parameters of these networks to be the same to enable a valid comparison between multiple models. As shown in Table 2, the learning rate was set to 0.04, the image size was set to 640 × 640, the batch size was set to 32, and the training epoch was set to 200. The optimizer used was a stochastic gradient descent. Additionally, YOLOv5s did not use pretrained weights to ensure a fair comparison between all models. The confidence threshold of the predicted boxes represents the probability that the model believes there is an object within that box. This paper uses the value of 0.9, and only when the confidence of a predicted box is above 0.9 is it considered an accepted and valid detection result. The hyperparameters used were based on the settings in the ‘hyp.scratch-low.yaml’ file in YOLOv5s, and all other parameters were set to their default values.

3.4. Comparison with State-of-the-Art Methods

In this study, we compared the improved method with state-of-the-art object detection models (SSD [65], Faster R-CNN [66], RetinaNet [67], Mask-RCNN [68], Fully convolutional networks (FCN) [69], and DeepLabv3 [70]). Faster R-CNN consists of a region proposal network and classification and regression networks and is considered the best model for the two-stage model. The SSD is composed of VGG16 [71] and a one-stage detector based on a feature pyramid. RetinaNet is composed of ResNet and a one-stage detector and employs FPN to increase the algorithm’s detection performance. Mask R-CNN extends Faster R-CNN and can perform both object detection and instance segmentation [68]. FCN is a network composed of fully convolutional layers, which can achieve end-to-end image segmentation [69]. DeepLabv3 is a network that uses techniques such as Atrous Spatial Pyramid Pooling to enhance segmentation accuracy [70]. The six models achieved more satisfactory results in object detection and were therefore selected and compared with our approach [65,66,67].

3.5. Performance Evaluation

The mean average precision (mAP) and confusion matrix were used independently to assess the effectiveness of the pig detection model. The confusion matrix is a standard format for representing the classification accuracy of a model and includes the number of identifications that were false-negative (FN), false-positive (FP), true negative (TN) or true positive (TP). TP indicates the number of pigs correctly predicted, FP indicates the number of pigs incorrectly predicted as the background, FN indicates the number of backgrounds incorrectly predicted as pigs, and TN indicates the number of backgrounds correctly predicted. Using the results of the confusion matrix are two evaluation measures, precision (Equation (1)). Precision represents the proportion of correctly identified pig pictures to all identified pig pictures.

(1)$\mathrm{Precision}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}$

In addition, mAP represents the mean average precision for each category (Equation (2)). We selected [email protected] and [email protected]:0.95 evaluation metrics to more accurately reflect the model performance. The mAP value is indicated by [email protected], and the average mAP at various IoU thresholds is indicated by [email protected]:0.95 (from 0.5 to 0.95). These accuracy metrics reflect the accuracy of pig and background differentiation in images from different angles. Global acc represents the proportion of correctly predicted pixels to the total number of pixels (Equation (3)). In this context, n represents the number of categories, and ${\mathrm{p}}_{\mathrm{ij}}$ represents the element in the i-th row and j-th column of the confusion matrix, which indicates the number of pixels that are predicted as category j but truly belong to category i.

(2)$\mathrm{mAP}=\frac{1}{\mathrm{n}}\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathrm{AP}}_{\mathrm{i}}$

(3)$\mathrm{GlobalACC}=\frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{p}}_{\mathrm{ii}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{p}}_{\mathrm{ij}}}$

4. Results

4.1. Two-Dimensional Pig Image Detection and Segmentation Results

We conducted a comparative analysis of six networks for detection and segmentation tasks, including three detection networks (SSD, Faster R-CNN, and RetinaNet) and three segmentation networks (FCN, DeepLabv3, and Mask RCNN). Figure 9a,b shows the training curves of different detection and segmentation models, respectively, on the [email protected]:0.95 metric for our dataset. Similarly, Figure 9c,d shows the training curves for different detection and segmentation models on the [email protected]:0.95 metric for the UK dataset.

From Figure 9a,b, we can observe that the improved YOLOv5s_Res2Net model outperforms YOLOv5s, SSD, Faster R-CNN, and RetinaNet on the mAP metric, achieving higher detection accuracy. In addition, although YOLOv5s_Res2Net fluctuates a lot in the early stages of training, it gradually decreases and stabilises in the later stages of training. From Figure 9c,d, we can observe that the improved YOLOv5s_Res2Net model outperforms YOLOv5s, FCN, DeepLabv3, and Mask RCNN on the [email protected]:0.95 metric, achieving higher segmentation accuracy. Similar to the YOLOv5s_Res2Net detection model, its segmentation model has a slow convergence speed and is unstable in the early stage of training, but gradually stabilizes in the later stage of training. In summary, the YOLOv5s_Res2Net model shows better performance in pig detection and segmentation tasks. Additionally, by analysing Figure 9a–d, we can observe that in detection and segmentation tasks on different datasets, the YOLOv5 algorithm consistently maintains a stable model performance compared to the other six algorithms. For example, in Figure 9a,b, the RetinaNet algorithm shows a large performance gap between two different datasets.

This experiment used the mAP metric to evaluate both detection and segmentation tasks. Table 3 shows the results of pig detection and segmentation on our dataset and the UK dataset using YOLOv5s_Res2Net, YOLOv5s, SSD, RetinaNet, Faster R-CNN, FCN, DeepLabv3, and Mask-RCNN methods. In our dataset, YOLOv5s_Res2Net achieved 99.5% and 99.5% [email protected] for pig detection and segmentation tasks, respectively, and 89.6% and 84.8% [email protected]:0.95, representing an improvement of 4.3% and 3.2% [email protected]:0.95, respectively, compared to the YOLOv5s algorithm. In the UK dataset, YOLOv5s_Res2Net achieved 99.4% and 99.4% [email protected] for pig detection and segmentation tasks, respectively, and 93.4% and 89.4% [email protected]:0.95, representing an improvement of 2.9% and 2.2% [email protected]:0.95, respectively, compared to the YOLOv5s algorithm. Moreover, we validated the effectiveness of the Mosaic algorithm on the UK dataset using YOLOv5s. The experimental results demonstrate that incorporating the Mosaic algorithm into YOLOv5s improved its detection and segmentation performance by 2.4% and 2.1% in terms of [email protected]:0.95, respectively.

Additionally, Table 3 also shows the performance comparison of YOLOv5s_Res2Net with other algorithms on two tasks. For the detection task, in our dataset, YOLOv5s_Res2Net demonstrated an improvement of 1.0%, 14.8%, 3.6%, and 5.8% on the [email protected] metric and 20.0%, 34.9 12.9%, and 16.6% on the [email protected]:0.95 metric compared to the Faster R-CNN, SSD, RetinaNet, and Mask-RCNN algorithms, respectively. In the UK dataset, YOLOv5s_Res2Net improved by 0.4%, 1.2%, 3.1%, and 3.7% on the [email protected] metric and by 7.4%, 23.4%, 24.1%, and 9.3% on the [email protected]:0.95 metric compared to the Faster R-CNN, SSD, RetinaNet, and Mask-RCNN algorithms, respectively. For the segmentation task, in our dataset, YOLOv5s_Res2Net improved by 5.1%, 7.7%, and 6.8% on the [email protected]:0.95 metric compared to the FCN, DeepLabv3, and Mask-RCNN algorithms, respectively. In the UK dataset, YOLOv5s_Res2Net showed an improvement of 0.1%, 1.6%, and 7.1% on the [email protected]:0.95 metric compared to the FCN, DeepLabv3, and Mask-RCNN algorithms, respectively. It can be observed that YOLOv5s_Res2Net performs significantly better than other algorithms on both metrics. Figure 10 shows some pig images and their detection and segmentation results.

Figure 10 presents the detection and segmentation results of eight algorithms on data collected from five different time periods. From the figure, it can be observed that YOLOv5s_Res2Net accurately locates pigs. However, YOLOv5 produces more duplicate and incorrect detections when images are collected at 12 o’clock. Faster R-CNN has a high number of false detections, which negatively impacts the overall performance of the model. RetinaNet performs well on images with good lighting and no overlap but struggles with images that have poor lighting and overlap, resulting in missed and duplicate detections. The SSD algorithm outperforms both Faster R-CNN and RetinaNet in terms of detection performance. Figure 11 presents the detection and segmentation results of two algorithms, YOLOv5s and YOLOv5s_Res2Net, on the UK dataset. From the figure, it can be observed that YOLOv5s_Res2Net possesses more fine-grained image features compared to YOLOv5s, enabling better detection and segmentation of pigs.

In summary, YOLOv5s_Res2Net accurately identifies the location and outline of pigs. When compared to six other detection and segmentation algorithms, YOLOv5s_Res2Net demonstrates superior performance in detection and segmentation tasks under varying lighting conditions.

4.2. Three-Dimensional Point Cloud Based on Pig Segmentation Mask and Depth Information

We obtain 3D point clouds of pigs by 2D detection and segmentation. To assess the consistency between our method and manual annotation, we applied the ICP algorithm to align the two types of point clouds and computed their similarity scores. Figure 12 displays the comparison results between the 3D point clouds generated by our pig segmentation method (left) and the manually annotated point clouds (right). Figure 13 presents a bar chart depicting the similarity between our method and the manually annotated point cloud, with an average similarity of 95.3%. These results indicate that our method is highly accurate and reliable.

5. Discussion

5.1. Advantages and Limitations

Our method offers several advantages: (1) It employs a single YOLOv5 model to perform both detection and segmentation tasks. (2) It leverages mature 2D image technology to rapidly complete 3D point cloud detection and segmentation tasks. (3) The training difficulty and detection speed of our 3D point cloud detection and segmentation method are comparable to those of a 2D detection and segmentation model. (4) Our method effectively avoids the generation of redundant point clouds due to human errors.

However, our method relies on 2D detection results, which means that the effectiveness of point cloud detection and segmentation is limited by the performance of the 2D detection task. As illustrated in Figure 14, when there is excessive adhesion of the target, the performance of the detection and segmentation models will decrease.

5.2. Generalization of the Model

Considering the potential applications of this research in the pig farming industry, we tested models trained on different datasets on another dataset. Figure 15 shows the detection and segmentation plots of the two models on our dataset and the UK dataset, respectively. From the image, we can observe that models trained on different datasets have some issues recognising each other’s images. These issues include incomplete segmentation of pigs’ edges, inaccurate identification of incomplete pig targets, and misidentification of other objects in the pigsty as pigs. These problems indicate that our method’s generalisation ability is not strong enough to recognise pig images with backgrounds of cement floors and grass. However, we believe that with a sufficient number of training samples, the generalisation ability of our model can be effectively improved, enabling our method to be applicable to datasets with different backgrounds and improving the accuracy of pig recognition in various backgrounds.

6. Conclusions

This paper proposes a detection and segmentation algorithm that is based on YOLOv5s. The algorithm enhances the fine-grained feature extraction ability of the backbone network by using a Res2Net bottleneck on the basis of YOLOv5s and significantly improves the detection and segmentation performance of the model in complex environments (variations in lighting). The experimental results demonstrated that the improved YOLOv5s_Res2Net achieved a [email protected] of 99.5% for both pig detection and segmentation tasks on our dataset, while achieving a [email protected]:0.95 of 89.6% and 84.8%, respectively. On the Edinburgh pig behaviour dataset, YOLOv5s_Res2Net obtained a [email protected] of 99.4% for both tasks, along with a [email protected]:0.95 of 93.4% and 89.4%. In comparison, YOLOv5s_Res2Net outperformed Faster R-CNN, SSD, RetinaNet, FCN, DeepLabv3, and Mask-RCNN algorithms.

In addition, we obtained detection and segmentation masks for RGB-D data using 2D detection and segmentation algorithms to obtain 3D point cloud data of multiple pigs simply and efficiently, with an average similarity of 95.3% compared to manually labelled point cloud data. This method uses mature 2D computer vision techniques, avoiding the tedious task of 3D data annotation and the high-performance hardware required for deep learning training based on 3D data, and can quickly and efficiently analyze the 3D point cloud data of pigs. In future research, we will implement point cloud reconstruction and weight estimation of multiple pigs based on this algorithm to provide technical support for intelligent farming.

Author Contributions

Proposed thoughts, created algorithm, confirmed results, and wrote manuscript, S.W.; Resources, supervision, and writing—review and editing, H.J.; Supervision, proposed thoughts, experimental design, analyzed data, and writing—review and editing, Y.Q.; Supervision, experimental design, and analyzed data, S.J. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The authors do not have permission to share data. The code implementation for this article link is: https://github.com/Shunli-W/2D-3D-D-S.git, 7 June 2023.

Acknowledgments

The pig farm in Fanzhuang Village, Dongping County, Tai’an City, Shandong Province, China, has our sincere gratitude. In addition, special thanks to Rongjia Li, Zheng Li, and Peidong Li for providing the data collection sites. Finally, we would like to thank Weiren Yang, Wangtao Zhan, and Jie Liu for their sites.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

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Figures and Tables

Figure 1. The whole data acquisition platform.

Figure 2. Acquired pig image examples in one day. The corresponding times are 09:00, 12:00, 16:00, 17:00, 17:20 and 17:30.

Figure 3. Edinburgh (UK) pig dataset.

Figure 4. Data after mosaic data enhancement.

Figure 5. The fast 3D pig detection based on the improved YOLOv5s model structure.

Figure 6. Res2Net bottleneck.

Figure 7. Three-dimensional point cloud multi-pig instance detection and segmentation. The coloured boxes represent different detection instances.

Figure 8. Pig segmentation labeling.

Figure 9. [email protected]:0.95 for the detection algorithm on our dataset (a). [email protected]:0.95 for the detection algorithm on UK dataset (b). [email protected]:0.95 for the segmentation algorithm on our dataset (c). [email protected]:0.95 for the segmentation algorithm on UK dataset (d).

Figure 10. Detection results of improved YOLOv5s, Faster R-CNN, RetinaNet, SSD, FCN, DeepLabv3 and Mask-RCNN for images of the same pig at different times in the same pigsty. The different colours in the figure only represent the results generated by different models.

Figure 11. Detection segmentation plots of YOLOv5s and YOLOv5s_Res2Net on the UK dataset.

Figure 12. The comparison results between the 3D point clouds of pig segmentation (left and green) and the manually annotated point clouds (right and pink).

Figure 13. The similarity bar chart of our method in point cloud detection and segmentation.

Figure 14. Unsuccessful detection and segmentation of pigs.

Figure 15. Models trained on different datasets recognise additional dataset’s images.

Table 1

The computer’s experimental equipment.

Hardware	Type
CPU	11th Gen Intel${}^{\circledR }$ Core${}^{\mathrm{TM}}$ i7-11700 @ 2.50 GHz
GPU	NVIDIA GeForce GTX 1080Ti
Memory	16.0 GB
Video Memory	11.0 GB
Hard disk	4 TB

Table 2

Training parameters for the models.

Parameters	Value	Remarks
Image size	640	Shrank image size to 640 × 640 during training.
Batch size	32	Ensured maximum batch size with limited graphics card memory.
Training epochs	100	Ensured that network was well-trained.
Optimiser	SGD	Selected suitable optimiser with limited graphics card memory.
Learning rate	0.04	Learning rate during model training.
Initial weights	Random	No pretraining weights were used.
Predict_conf_thres	0.9	The confidence threshold of predict boxse.
Hyperparameter	hyp.scratch-low.yaml	Hyperparameter file in YOLOv5s.

Table 3

Comparison of different detection methods (%).

Authors, Year	Methods	Datasets	mAP@ 0.5-D	mAP@ 0.5:0.95-D	mAP@ 0.5-S	mAP@ 0.5:0.95-S	Global ACC
Ren et al., 2015 [66]	Faster R-CNN	Our dataset	94.5	69.0	✔	✔	✔
Liu et al., 2016 [65]	SSD	Our dataset	84.7	54.7	✔	✔	✔
Lin et al., 2017 [67]	RetinaNet	Our dataset	95.6	76.7	✔	✔	✔
Ren et al., 2015 [66]	Faster R-CNN	UK dataset	99.0	86.0	✔	✔	✔
Liu et al., 2016 [65]	SSD	UK dataset	98.2	70.0	✔	✔	✔
Lin et al., 2017 [67]	RetinaNet	UK dataset	96.3	70.7	✔	✔	✔
Long et al., 2015 [69]	FCN	Our dataset	✔	✔	✔	79.7	94.6
Chen et al., 2017 [70]	DeepLabv3	Our dataset	✔	✔	✔	77.1	93.0
He et al., 2017 [68]	Mask-RCNN	Our dataset	93.7	73.0	92.6	78.0	✔
Long et al., 2015 [69]	FCN	UK dataset	✔	✔	✔	89.3	97.1
Chen et al., 2017 [70]	DeepLabv3	UK dataset	✔	✔	✔	87.8	96.2
He et al., 2017 [68]	Mask-RCNN	UK dataset	95.7	84.1	93.3	82.3	✔
Jocher et al., 2022 [62]	YOLOv5s	Our dataset	99.4	85.3	99.4	81.6	✔
Our work	YOLOv5s_Res2Net	Our dataset	99.5	89.6	99.5	84.8	✔
Jocher et al., 2022 [62]	YOLOv5s	UK dataset	99.3	90.5	99.3	87.2	✔
Our work	YOLOv5s_Res2Net	UK dataset	99.4	93.4	99.4	89.4	✔

References

1. de Paulo Farias, D.; dos Santos Gomes, M.G. COVID-19 outbreak: What should be done to avoid food shortages?. Trends Food Sci. Technol.; 2020; 102, 291. [DOI: https://dx.doi.org/10.1016/j.tifs.2020.06.007]

2. Qiao, Y.; Truman, M.; Sukkarieh, S. Cattle segmentation and contour extraction based on Mask R-CNN for precision livestock farming. Comput. Electron. Agric.; 2019; 165, 104958. [DOI: https://dx.doi.org/10.1016/j.compag.2019.104958]

3. Luo, X.; Yang, R.; Xu, Q. Spatial mismatch evolution of global population and food and its influencing factors. J. Nat. Resour.; 2021; 36, pp. 1381-1397. [DOI: https://dx.doi.org/10.31497/zrzyxb.20210603]

4. Woonwong, Y.; Do Tien, D.; Thanawongnuwech, R. The future of the pig industry after the introduction of African swine fever into Asia. Anim. Front.; 2020; 10, pp. 30-37. [DOI: https://dx.doi.org/10.1093/af/vfaa037]

5. OECDFood and Agriculture Organization of the United Nations. OECD-FAO Agricultural Outlook 2022–2031; OECD Publishing: Paris, France, 2022; 363p. [DOI: https://dx.doi.org/10.1787/f1b0b29c-en]

6. Qiao, Y.; Kong, H.; Clark, C.; Lomax, S.; Su, D.; Eiffert, S.; Sukkarieh, S. Intelligent perception for cattle monitoring: A review for cattle identification, body condition score evaluation, and weight estimation. Comput. Electron. Agric.; 2021; 185, 106143. [DOI: https://dx.doi.org/10.1016/j.compag.2021.106143]

7. Benjamin, M.; Yik, S. Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners. Animals; 2019; 9, 133. [DOI: https://dx.doi.org/10.3390/ani9040133]

8. Wang, S.; Jiang, H.; Qiao, Y.; Jiang, S.; Lin, H.; Sun, Q. The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming. Sensors; 2022; 22, 6541. [DOI: https://dx.doi.org/10.3390/s22176541]

9. Zhang, L.; Gray, H.; Ye, X.; Collins, L.; Allinson, N. Automatic Individual Pig Detection and Tracking in Pig Farms. Sensors; 2019; 19, 1188. [DOI: https://dx.doi.org/10.3390/s19051188]

10. Guo, Q.; Sun, Y.; Min, L.; van Putten, A.; Knol, E.; Visser, B.; Rodenburg, T.; Bolhuis, L.; Bijma, P.; de With, P.H.N. Video-based Detection and Tracking with Improved Re-Identification Association for Pigs and Laying Hens in Farms. Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications; Online, 6–8 February 2022; Farinella, G.; Radeva, P.; Bouatouch, K. SciTePress: Setúbal, Portugal, 2022; [DOI: https://dx.doi.org/10.5220/0010788100003124]

11. Cowton, J.; Kyriazakis, I.; Bacardit, J. Automated Individual Pig Localisation, Tracking and Behaviour Metric Extraction Using Deep Learning. IEEE Access; 2019; 7, pp. 108049-108060. [DOI: https://dx.doi.org/10.1109/ACCESS.2019.2933060]

12. Qiao, Y.; Chai, L.; He, D.; Su, D. Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming; MDPI: Basel, Switzerland, 2022.

13. Yang, Q.; Xiao, D.; Lin, S. Feeding behavior recognition for group-housed pigs with the Faster R-CNN. Comput. Electron. Agric.; 2018; 155, pp. 453-460. [DOI: https://dx.doi.org/10.1016/j.compag.2018.11.002]

14. Riekert, M.; Klein, A.; Adrion, F.; Hoffmann, C.; Gallmann, E. Automatically detecting pig position and posture by 2D camera imaging and deep learning. Comput. Electron. Agric.; 2020; 174, 105391. [DOI: https://dx.doi.org/10.1016/j.compag.2020.105391]

15. Yin, D.; Tang, W.; Chen, P.; Yang, B. Pig Target Detection from Image Based on Improved YOLO V3. Advances in Artificial Intelligence and Security, Proceedings of the 7th International Conference, ICAIS 2021, Dublin, Ireland, 19–23 July 2021; Sun, X.; Zhang, X.; Xia, Z.; Bertino, E. Springer: Cham, Switzerland, 2021; pp. 94-104. [DOI: https://dx.doi.org/10.1007/978-3-030-78615-1_9]

16. Ahn, H.; Son, S.; Kim, H.; Lee, S.; Chung, Y.; Park, D. EnsemblePigDet: Ensemble Deep Learning for Accurate Pig Detection. Appl. Sci.; 2021; 11, 5577. [DOI: https://dx.doi.org/10.3390/app11125577]

17. Wutke, M.; Heinrich, F.; Das, P.P.; Lange, A.; Gentz, M.; Traulsen, I.; Warns, F.K.; Schmitt, A.O.; Gültas, M. Detecting Animal Contacts—A Deep Learning-Based Pig Detection and Tracking Approach for the Quantification of Social Contacts. Sensors; 2021; 21, 7512. [DOI: https://dx.doi.org/10.3390/s21227512]

18. Bo, Z.; Atif, O.; Lee, J.; Park, D.; Chung, Y. Gan-based video denoising with attention mechanism for field-applicable pig detection system. Sensors; 2022; 22, 3917. [DOI: https://dx.doi.org/10.3390/s22103917]

19. Kim, J.; Suh, Y.; Lee, J.; Chae, H.; Ahn, H.; Chung, Y.; Park, D. EmbeddedPigCount: Pig Counting with Video Object Detection and Tracking on an Embedded Board. Sensors; 2022; 22, 2689. [DOI: https://dx.doi.org/10.3390/s22072689]

20. Xiao, D.; Lin, S.; Liu, Y.; Yang, Q.; Wu, H. Group-housed pigs and their body parts detection with Cascade Faster R-CNN. Int. J. Agric. Biol. Eng.; 2022; 15, pp. 203-209. [DOI: https://dx.doi.org/10.25165/j.ijabe.20221503.6286]

21. Tu, S.; Yuan, W.; Liang, Y.; Wang, F.; Wan, H. Automatic detection and segmentation for group-housed pigs based on PigMS R-CNN. Sensors; 2021; 21, 3251. [DOI: https://dx.doi.org/10.3390/s21093251]

22. Hu, Z.; Yang, H.; Lou, T. Dual attention-guided feature pyramid network for instance segmentation of group pigs. Comput. Electron. Agric.; 2021; 186, 106140. [DOI: https://dx.doi.org/10.1016/j.compag.2021.106140]

23. Zhai, X.; Tian, J.; Li, J. Instance segmentation method of adherent targets in pig images based on improved mask R-CNN. Proceedings of the 2021 33rd Chinese Control and Decision Conference (CCDC); Kunming, China, 22–24 May 2021; pp. 368-373.

24. Liu, C.; Su, J.; Wang, L.; Lu, S.; Li, L. LA-DeepLab V3+: A Novel Counting network for pigs. Agriculture; 2022; 12, 284. [DOI: https://dx.doi.org/10.3390/agriculture12020284]

25. Lu, J.; Wang, W.; Zhao, K.; Wang, H. Recognition and segmentation of individual pigs based on Swin Transformer. Anim. Genet.; 2022; 53, pp. 794-802. [DOI: https://dx.doi.org/10.1111/age.13259]

26. Gan, H.; Ou, M.; Li, C.; Wang, X.; Guo, J.; Mao, A.; Ceballos, M.C.; Parsons, T.D.; Liu, K.; Xue, Y. Automated detection and analysis of piglet suckling behaviour using high-accuracy amodal instance segmentation. Comput. Electron. Agric.; 2022; 199, 107162. [DOI: https://dx.doi.org/10.1016/j.compag.2022.107162]

27. Zhong, Z. A novel visible and infrared image fusion method based on convolutional neural network for pig-body feature detection. Multimed. Tools Appl.; 2022; 81, pp. 2757-2775. [DOI: https://dx.doi.org/10.1007/s11042-021-11675-5]

28. Song, Y.; Jin, T.; Dai, Y.; Song, Y.; Zhou, X. Through-wall human pose reconstruction via UWB MIMO radar and 3D CNN. Remote Sens.; 2021; 13, 241. [DOI: https://dx.doi.org/10.3390/rs13020241]

29. Zhao, H.; Yang, D.; Yu, J. 3D target detection using dual domain attention and SIFT operator in indoor scenes. Vis. Comput.; 2022; 38, pp. 3765-3774. [DOI: https://dx.doi.org/10.1007/s00371-021-02217-z]

30. Liang, Z.; Chen, C.; Chen, Y.; Chen, G.; Liu, G. Point cloud classification and segmentation network based on double local features fusion of high-dimensional and low-dimensional. Laser Infrared; 2022; 52, pp. 1557-1564.

31. Camuffo, E.; Mari, D.; Milani, S. Recent Advancements in Learning Algorithms for Point Clouds: An Updated Overview. Sensors; 2022; 22, 1357. [DOI: https://dx.doi.org/10.3390/s22041357]

32. Su, H.; Maji, S.; Kalogerakis, E.; Learned-Miller, E. Multi-view convolutional neural networks for 3d shape recognition. Proceedings of the IEEE International Conference on Computer Vision; Santiago, Chile, 13–16 December 2015; pp. 945-953.

33. Feng, Y.; Zhang, Z.; Zhao, X.; Ji, R.; Gao, Y. GVCNN: Group-view convolutional neural networks for 3d shape recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Salt Lake City, UT, USA, 18–23 June 2018; pp. 264-272.

34. Wu, B.; Wan, A.; Yue, X.; Keutzer, K. SqueezeSeg: Convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3D LiDAR point cloud. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA); Brisbane, QLD, Australia, 21–25 May 2018; pp. 1887-1893.

35. Maturana, D.; Scherer, S. Voxnet: A 3D convolutional neural network for real-time object recognition. Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Hamburg, Germany, 28 September–2 October 2015; pp. 922-928.

36. Tchapmi, L.; Choy, C.; Armeni, I.; Gwak, J.; Savarese, S. Segcloud: Semantic segmentation of 3D point clouds. Proceedings of the 2017 International Conference on 3D Vision (3DV); Qingdao, China, 10–12 October 2017; pp. 537-547.

37. Shi, S.; Wang, X.; Li, H. PointRCNN: 3D object proposal generation and detection from point cloud. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; Long Beach, CA, USA, 16–20 June 2019; pp. 770-779.

38. Yang, Z.; Sun, Y.; Liu, S.; Shen, X.; Jia, J. STD: Sparse-to-dense 3D object detector for point cloud. Proceedings of the IEEE/CVF International Conference on Computer Vision; Seoul, Republic of Korea, 27 October–2 November 2019; pp. 1951-1960.

39. Xie, J.; Zheng, Z.; Gao, R.; Wang, W.; Zhu, S.C.; Wu, Y.N. Generative VoxelNet: Learning energy-based models for 3D shape synthesis and analysis. IEEE Trans. Pattern Anal. Mach. Intell.; 2020; 44, pp. 2468-2484. [DOI: https://dx.doi.org/10.1109/TPAMI.2020.3045010]

40. Qi, C.R.; Su, H.; Mo, K.; Guibas, L.J. PointNet: Deep learning on point sets for 3D classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Honolulu, HI, USA, 21–26 July 2017; pp. 652-660.

41. Qi, C.R.; Yi, L.; Su, H.; Guibas, L.J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Adv. Neural Inf. Process. Syst.; 2017; 30, pp. 1-10.

42. Zhao, H.; Jiang, L.; Fu, C.W.; Jia, J. PointWeb: Enhancing local neighborhood features for point cloud processing. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; Long Beach, CA, USA, 15–20 June 2019; pp. 5565-5573.

43. Wu, W.; Qi, Z.; Fuxin, L. PointConv: Deep convolutional networks on 3D point clouds. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; Long Beach, CA, USA, 15–20 June 2019; pp. 9621-9630.

44. Wang, Y.; Sun, Y.; Liu, Z.; Sarma, S.E.; Bronstein, M.M.; Solomon, J.M. Dynamic graph cnn for learning on point clouds. ACM Trans. Graph.; 2019; 38, pp. 1-12. [DOI: https://dx.doi.org/10.1145/3326362]

45. Lu, Q.; Chen, C.; Xie, W.; Luo, Y. PointNGCNN: Deep convolutional networks on 3D point clouds with neighborhood graph filters. Comput. Graph.; 2020; 86, pp. 42-51. [DOI: https://dx.doi.org/10.1016/j.cag.2019.11.005]

46. Lan, H.; Chen, H.; Zhang, P. Point Cloud Classification and Segmentation Model Based on Graph Convolution and 3D Direction Convolution. Comput. Eng. Appl.; 2023; 59, pp. 182-191.

47. Zhang, K.; Zhu, Y.; Wang, X.; Zhang, Y.; Zhong, R. Three-Dimensional Point Cloud Semantic Segmentation Network Based on Spatial Graph Convolution Network. Laser Optoelectron. Prog.; 2023; 60, pp. 387-395.

48. Chen, C.; Fragonara, L.Z.; Tsourdos, A. GAPNet: Graph attention based point neural network for exploiting local feature of point cloud. arXiv; 2019; arXiv: 1905.08705

49. Yang, J.; Dang, J. Semantic segmentation of 3D point cloud based on contextual attention CNN. J. Commun.; 2020; 7, pp. 195-203.

50. Hu, Q.; Yang, B.; Xie, L.; Rosa, S.; Guo, Y.; Wang, Z.; Trigoni, N.; Markham, A. RandLA-Net: Efficient semantic segmentation of large-scale point clouds. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; Online, 14–19 June 2020; pp. 11108-11117.

51. Wu, J.; Cui, Y.; Zhao, X.; Chen, R.; Xu, G. SSA-PointNet++: A Space Self-Attention CNN for the Semantic Segmentation of 3D Point Cloud. J. Comput.-Aided Des. Comput. Graph.; 2022; 34, pp. 437-448.

52. Vanian, V.; Zamanakos, G.; Pratikakis, I. Improving performance of deep learning models for 3D point cloud semantic segmentation via attention mechanisms. Comput. Graph.; 2022; 106, pp. 277-287. [DOI: https://dx.doi.org/10.1016/j.cag.2022.06.010]

53. Liu, H.; Huo, G.; Li, Q.; Guan, X.; Tseng, M.L. Multiscale lightweight 3D segmentation algorithm with attention mechanism: Brain tumor image segmentation. Expert Syst. Appl.; 2023; 214, 119166. [DOI: https://dx.doi.org/10.1016/j.eswa.2022.119166]

54. Ma, Y.; Chen, X.; Zhang, W.; Zheng, L.; Gao, W.; Wang, M. An Approach of On-barn Pig Weight Estimation via 3D Computer Vision by Kinect V2. Proceedings of the 2020 American Society of Agricultural and Biological Engineers (ASABE) Annual International Virtual Meeting; Online, 13–15 July 2020; 1.

55. Li, G.; Liu, X.; Ma, Y.; Wang, B.; Zheng, L.; Wang, M. Body size measurement and live body weight estimation for pigs based on back surface point clouds. Biosyst. Eng.; 2022; 218, pp. 10-22. [DOI: https://dx.doi.org/10.1016/j.biosystemseng.2022.03.014]

56. Yu, H.; Lee, K.; Morota, G. Forecasting dynamic body weight of nonrestrained pigs from images using an RGB-D sensor camera. Transl. Anim. Sci.; 2021; 5, txab006. [DOI: https://dx.doi.org/10.1093/tas/txab006]

57. Zhang, J.; Zhuang, Y.; Ji, H.; Teng, G. Pig weight and body size estimation using a multiple output regression convolutional neural network: A fast and fully automatic method. Sensors; 2021; 21, 3218. [DOI: https://dx.doi.org/10.3390/s21093218]

58. Nguyen, A.H.; Holt, J.P.; Knauer, M.T.; Abner, V.A.; Lobaton, E.J.; Young, S.N. Towards rapid weight assessment of finishing pigs using a handheld, mobile RGB-D camera. Biosyst. Eng.; 2023; 226, pp. 155-168. [DOI: https://dx.doi.org/10.1016/j.biosystemseng.2023.01.005]

59. Liu, T.; Teng, G.; Zhang, S.; Li, Z.; Guo, P. Reconstruction and application of 3D pig body model based on point cloud data. Trans. Chin. Soc. Agric. Mach.; 2014; 45, pp. 291-295.

60. He, H.; Qiao, Y.; Li, X.; Chen, C.; Zhang, X. Automatic weight measurement of pigs based on 3D images and regression network. Comput. Electron. Agric.; 2021; 187, 106299. [DOI: https://dx.doi.org/10.1016/j.compag.2021.106299]

61. Yi, Y.; Peng, Y.; Qi, J. Research on image similarity retrieval algorithm based on dual hash. Inf. Commun. Technol.; 2019; 13, pp. 33-38.

62. Jocher, G.; Chaurasia, A.; Stoken, A.; Borovec, J. NanoCode012 Kwon, Y. TaoXie Fang, J. imyhxy Michael, K. et al. ultralytics/yolov5: V6.1—TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference. Zenodo; 2022; [DOI: https://dx.doi.org/10.5281/zenodo.6222936]

63. Gao, S.H.; Cheng, M.M.; Zhao, K.; Zhang, X.Y.; Yang, M.H.; Torr, P. Res2net: A new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell.; 2019; 43, pp. 652-662. [DOI: https://dx.doi.org/10.1109/TPAMI.2019.2938758]

64. Labelme; Version 5.0.1 Massachusetts Institute of Technology: Cambridge, MA, USA, 2023; Available online: http://labelme.csail.mit.edu (accessed on 7 June 2023).

65. Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.; Fu, C.Y.; Berg, A.C. SSD: Single shot multibox detector. Computer Vision—ECCV 2016, Proceedings of the 14th European Conference, Amsterdam, The Netherlands, 11–14 October 2016; Leibe, B.; Matas, J.; Sebe, N.; Welling, M. Springer: Cham, Switzerland, 2016; Volume 9905, pp. 21-37. [DOI: https://dx.doi.org/10.1007/978-3-319-46448-0_2]

66. Ren, S.; He, K.; Girshick, R.; Sun, J. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell.; 2017; 39, pp. 1137-1149. [DOI: https://dx.doi.org/10.1109/TPAMI.2016.2577031]

67. Lin, T.Y.; Goyal, P.; Girshick, R.; He, K.; Dollár, P. Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell.; 2020; 42, pp. 318-327. [DOI: https://dx.doi.org/10.1109/TPAMI.2018.2858826]

68. He, K.; Gkioxari, G.; Dollár, P.; Girshick, R. Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision; Venice, Italy, 22–29 October 2017; pp. 2961-2969.

69. Long, J.; Shelhamer, E.; Darrell, T. Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Boston, MA, USA, 7–12 June 2015; pp. 3431-3440.

70. Chen, L.C.; Papandreou, G.; Schroff, F.; Adam, H. Rethinking atrous convolution for semantic image segmentation. arXiv; 2017; arXiv: 1706.05587

71. Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv; 2014; [DOI: https://dx.doi.org/10.48550/arXiv.1409.1556] arXiv: 1409.1556