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1. Introduction
Tunnels are an important part of the road and railway transportation network. With the continuous increase in the mileage of roads and railways in China, and due to the aging of the structure itself, a large number of tunnels have gradually entered the maintenance period. The tunnel itself is faced with its own structural aging and damage from external factors. Among many tunnel diseases, lining cracks are one of the fundamental problems that endanger the safe operation of the tunnel. It will not only affect the durability of the lining structure, but may even endanger the safety of the structure itself and cause major losses. If tunnel cracks are discovered and repaired in time at the initial stage, the probability of serious consequences will be greatly reduced. At present, manual detection is still the main way to find tunnel cracks, but this method is low in efficiency, is low in detection accuracy, and consumes a lot of manpower and material resources, and the overall detection effect is not satisfactory. With the development of tunnel inspection vehicles, it is possible to use a vehicle-mounted CCD camera to take pictures of the lining of a tunnel in a short time. Then, according to the obtained tunnel lining image, a detection method based on computer vision is used to identify the cracks in the image. Compared with the artificial tunnel crack detection method, the crack detection method based on computer vision has the advantages of fast recognition speed and high detection accuracy and has been widely used in tunnel crack detection [1–3].
Scholars at home and abroad have conducted a lot of research on the identification of tunnel lining cracks and have achieved satisfactory results. Li et al. [4] analyzed the existing problems of the existing tunnel crack detection system and creatively applied the latest technology to the moving train, which can realize the tunnel lining crack identification when the train speed is 50 km/h, but the overall crack identification accuracy is not high. In order to better identify and analyze cracks, Shi et al. [5] proposed an analysis method based on crack width characteristics, which greatly reduces the interference of “false cracks” and improves the accuracy of crack identification. Jang et al. [6] proposed a hybrid image autonomous concrete crack detection technology based on deep learning. The hybrid image combined with visual and infrared thermal imaging can improve the detection ability and detection accuracy of cracks. Win and Thein [7] proposed a related reverse method to identify the location and size of cracks in concrete structures. Nhat-Duc and Peir [8] used a gray intensity adjustment method called Min-Max Gray Level Discrimination (M2GLD) to automatically identify and analyze cracks. Jia et al. [9] proposed a multiscale spatial tunnel lining crack water leakage identification method based on point cloud images and proposed a fusion image detection operator, which can maintain the stability of band-shaped crack image detection. Existing various edge detection algorithms are not satisfactory for image recognition results, and the time and accuracy of crack extraction need to be further improved. Zhu et al. [10] realized the accurate identification of tunnel cracks by fusing the template-based analysis method, the linear structure analysis method based on Hough transform, and the approximate crack structure analysis method based on support vector machine, but the algorithm took a long time. Dai et al. [11] used improved homomorphic filtering to process the collected images and used the XDoG edge extraction method to extract the edges of the cracks in the image. Practice has proved that this method has the advantage of fast calculation speed. On the basis of structural similarity, Yan et al. [12] proposed an image quality evaluation method based on convolution filtering and gradient structural similarity. This method can obtain better evaluation results for dithered and blurred lining images with cracks. Although the existing image preprocessing methods are basically perfect, there are still environments with poor tunnel lighting, and image recognition needs to be enhanced.
In response to insufficient illumination and uneven illumination, Song et al. [13] proposed a multiscale joint low-light enhancement network based on Retinex theory to effectively solve the problems of noise amplification and color distortion. Sun et al. [14] proposed a nonuniform illumination correction algorithm based on multiscale Retinex, which enhanced the measurement accuracy of DIC. Wang et al. [15] designed a low-light image enhancement optimization algorithm based on frame accumulation and multiscale Retinex joint processing; this algorithm improves the overall quality of the image to a certain extent. For the problem of occurrence of halation and overenhancement, Ping et al. [16] fused Gabor filtering with Retinex theory. In order to solve the light scattering in the water and the interference of the impurities in the water on the light propagation, Huang et al. [17] combined the color correction method with the deblurring network, which can better eliminate the blur and enhance the details. Liu et al. [18] used MSRCR and guided filtering methods for defogging and proposed a white balance fusion global guided image filtering (G-GIF) method, which effectively solved the problem of edge detail loss.
Although the existing image preprocessing methods are basically perfect, there is still a lack of image filtering methods for the environment with poor tunnel illumination. Various existing edge detection algorithms are not satisfactory for image recognition results, and there are often more false cracks and noises, which affect the final recognition results of cracks. The time and accuracy of crack extraction need to be further improved. Based on the abovementioned problems, this paper proposes a new tunnel lining crack identification algorithm based on improved multiscale Retinex and Sobel edge detection.
2. Basic Principles of Retinex Algorithm
In 1978, Land [19] first proposed the calculation theory of color constancy perception, also known as the Retinex theory. Different from traditional linear and nonlinear methods that can only enhance a certain type of image feature, Retinex can strike a balance between dynamic range compression, edge enhancement, and color constancy. Therefore, various different types of images can be adaptively enhanced.
2.1. Single-Scale Retinex (SSR)
The Retinex theory can divide the image
[figure omitted; refer to PDF]
The basic principle of the Retinex algorithm is to estimate the reflected component through the incident component and then achieve the purpose of image enhancement. In 1997, Jobson improved the Retinex algorithm and proposed the SSR algorithm [21]. The basic idea of the single-scale Retinex algorithm consists of three steps. The first step is to construct a Gaussian surround function and then use the constructed function to filter the three color channels of R, G, and B of the color image. The filtered image is the incident component we estimated. The third step is to subtract the incident component from the original image in the logarithmic domain, and the data obtained is the reflection component, and the reflection component is the output result image. The formula of the SSR algorithm is shown in the following formula:
In formula (2),
In formula (3), where
2.2. Multiscale Retinex Algorithm (MSR)
Since the single-scale Retinex algorithm selects Gaussian function as the number of traverse segments, the enhanced image cannot simultaneously guarantee a large-scale compression of dynamic range and contrast intensity. In order to balance the two enhancement effects, a very accurate scale constant
In view of the shortcoming of the single-scale Retinex algorithm, some scholars have proposed the multiscale Retinex algorithm [22, 23]. Compared with the single-scale Retinex, the multiscale Retinex algorithm can take into account the two characteristics of dynamic range compression and image contrast. The MSR algorithm is a linear weighted summation of the single-scale Retinex algorithm, and its specific expression is shown in the following formula:
In formula (4),
2.3. The Improved MSR Algorithm
The traditional MSR algorithm uses a Gaussian function as the surround function, but because the Gaussian operator cannot estimate the illumination well in the transition zone, when the illumination changes greatly, a halo phenomenon will occur, which will weaken the image and affect the enhancement effect of the image. Therefore, this paper chooses to use bilateral filter function instead of the original Gaussian wrap function [24]. Compared with the Gaussian function, bilateral filtering can enhance the spatial domain while maintaining the smoothness of the time domain, which can better solve the problem of misjudgment of the Gaussian function. The edge information of the image can also be preserved, so that the obtained image is more continuous and smooth, and it can also solve the halo problem caused by the Gaussian function to a certain extent. The function of bilateral filtering is
Among them,
2.3.1. Gamma Correction
Gamma correction on the image after bilateral filtering can make the transformed image more natural. The specific expression form of gamma correction is shown in the following formula:
Among them,
2.3.2. Sigmoid Function
The sigmoid function can simulate the nonlinear and local characteristics of human vision to a certain extent and has good local details and dynamic range compression capabilities [25]. Assuming that the low-illuminance image
Using the Sigmoid function to stretch the contrast of the reflected image can enhance the contrast of the image and make the processed image more visible. The formula is shown in the following formula:
When the brightness component of the image is enhanced, the saturation of the image is lower. In order to make the image color more full, the saturation of the image needs to be stretched. However, because the tunnel images obtained under different lighting conditions have certain differences, the corresponding saturation processing levels must also be different. This paper proposes an adaptive nonlinear stretching algorithm for saturation components, and the formula is shown in the following equation:
Among them,
The overall flow of the algorithm in this paper is shown in Figure 2. First, extract the RGB three color channels of the image, and then convert the RGB color space to the HSV color space, and the H channel remains unchanged. The S channel is adaptively enhanced. The V channel first uses the improved MSR algorithm for image filtering. Then, use the gamma function for color correction. Finally, the HSV space is converted back to the RGB space, and Sigmoid function is used to improve the reflection component and enhance image details. So, we got the image preprocessing result. Next, the Sobel edge detection algorithm is used to detect the edges and remove the independent points in the image to make the crack shape more complete.
[figure omitted; refer to PDF]
As shown in Figure 6, the eight neighborhoods of Sector 3 and Sector 4 do not contain other circumscribed rectangles, so they are judged as isolated edges and discarded. However, Sector 1 and Sector 2 are nonisolated points, and the algorithm is reserved. It can be seen from Table 1 that after removing the isolated edges of the cracks, the number of noncharacteristic pixels can be reduced by 27%, and the final proportion of the number of cracks can reach 94%. The number of noise points in the image is obviously reduced, and the crack skeleton roughly appears.
Table 1
Comparison of information on the removal of isolated points of cracks.
Operation | Total number of feature extractions | Crack feature recognition number | Proportion of fracture characteristics (%) |
Before the algorithm | 14480 | 10253 | 70.8 |
After the algorithm | 10562 | 9930 | 94.1 |
3.3. Breakpoint Connection and Image Filling
The crack skeleton usually has a certain degree of discontinuity, and the crack image after the edge is extracted by the Sobel edge detection operator does not conform to the actual situation, so the crack needs to be filled. The direction vector crack connection algorithm proposed in the literature [3] is used to connect the broken points of the processed image, and the final result is shown in Figure 7. Then, fill the upper and lower white edges of Figure 7 as a whole. The specific effect is shown in Figure 8. It can be seen from Figures 7 and 8 that, after the breakpoint connection and crack filling operations, the crack image can already be identified, which can lay the foundation for other subsequent steps.
[figure omitted; refer to PDF]
The calculation formula of the recognition accuracy rate
In order to verify the accuracy of the algorithm, 200 tunnel crack images were randomly selected from the gallery for identification. If the cracks in the image can be completely identified, the identification is considered successful; otherwise, the identification fails. The image of cracks on the surface of ordinary concrete is selected for comparison with the cracks of the bridge, and the recognition results are shown in Table 3.
Table 3
Comparison of the accuracy of crack recognition.
Test object | Quantity | Recognition rate (%) |
Concrete crack | 200 | 99.0 |
Tunnel lining crack | 200 | 97.5 |
Bridge crack | 200 | 92.0 |
From the comparison in Table 3, it can be seen that the accuracy of the proposed tunnel lining crack recognition image in this paper can reach 97.5%, which is higher than other existing algorithms. The recognition effect of ordinary concrete surface cracks is also very good, which may be due to the better light obtained when collecting ordinary concrete surface cracks, and there is basically no other noise interference. However, the accuracy of identifying bridge cracks is still low. The bridge cracks here do not refer to the cracks in the asphalt pavement of the bridge, but the wings and piers of the bridge superstructure. These parts usually need to be added with waterproof paint, and there may be part of the paint peeling off in the obtained crack image, which leads to low recognition accuracy. The recognition rate of ordinary concrete crack images is higher than that of tunnel lining crack images, and bridge crack images have a certain relationship with light intensity. When the light is strong or weak, it will significantly affect the imaging of the crack edge and then have a greater impact on the identification of the crack width. After testing, when the light intensity is in the range of 1400–3100lux, the test accuracy of the width is relatively high, and the average relative error is less than 5% [27].
4.3. Algorithm Running Time Comparison
The running time of an algorithm is an important indicator to evaluate the efficiency of an algorithm. In the case of ensuring the recognition accuracy, the faster the algorithm runs, the higher the image recognition efficiency. Compare the algorithm proposed in this paper with literature [6], crack recognition algorithm based on maximum entropy [28], gradient-based improvement Canny algorithm [29], and SVM-based crack recognition algorithm [30] (the algorithm consumes time and takes the average of the test object). The calculation time comparison is shown in Table 4.
Table 4
Comparison of algorithm running time.
Algorithm | Running time (s) |
Document 6 algorithm | 5.2 |
Gradient-improved Canny algorithm | 1.2 |
Maximum entropy segmentation algorithm | 1.1 |
SVM-based crack recognition algorithm | 8.7 |
MSR-B algorithm | 4.1 |
From the comparison in Table 4, it can be seen that the gradient-improved Canny algorithm and the maximum entropy crack segmentation algorithm have the shortest running time, but because the algorithm itself does not preprocess the image through steps such as image filtering, the crack extraction results are relatively general. The SVM-based algorithm takes an average of 8.7 seconds for each picture, but the recognition result is not much different from this article. Compared with the algorithm in literature [6], the algorithm in this paper has a greater improvement in the processing efficiency of each image. It is suitable for a large number of tunnel image processing and has certain engineering application significance.
5. Conclusion
(1) The improved multiscale Retinex algorithm is used to filter the collected images of tunnel lining cracks. The addition of gamma transform and Sigmoid function can preserve more details of the image. Through the comparison of peak signal-to-noise ratio, information entropy, and contrast, it can be seen that the newly proposed image enhancement algorithm can well retain the original information of the image, and the edges of the image are preserved intact, which is conducive to subsequent recognition.
(2) The eight-direction Sobel operator is used to detect the edge of the image, and the isolated points are removed by the morphological operation and the principle of the smallest bounding rectangle, and finally the crack is filled. The results show that the proposed crack processing algorithm can make the crack identification accuracy reach 90.4% and can basically realize the complete identification of the main body of the crack.
(3) The crack recognition algorithm proposed in this paper has a recognition accuracy of 97.5%, and the calculation time for each picture is relatively short. Subsequent improvements can be made to the Sobel edge detection operator to reduce the recognition of error points. It is also possible to perform semantic segmentation of the image and then identify the segmented cracks to improve the recognition accuracy.
Authors’ Contributions
Wang Quanlei completed the writing of the main body of the thesis. Zhang Ning, Ma Chao, and Zhou Zhaochen mainly completed the programming work. Jiang Kun made certain revisions to the article as a whole. Dai Chunquan played an important guiding role in the article.
Acknowledgments
The authors are grateful to the financial support from the Humanities and Social Sciences Fund of the Ministry of Education (no. 20YJAZH022).
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Abstract
China is gradually transitioning from the “tunnel construction era” to the “tunnel maintenance era,” and more and more operating tunnels need to be inspected for diseases. With the continuous development of computer vision, the automatic identification of tunnel lining cracks with computers has gradually been applied in engineering. On the basis of summarizing the weaknesses and strengths of previous studies, this paper first uses the improved multiscale Retinex algorithm to filter the collected tunnel crack images and introduces the eight-direction Sobel edge detection operator to extract the edges of the cracks. Perform mathematical morphological operations on the image after edge extraction, and use the principle of the smallest enclosing rectangle to remove the isolated points of the image. Finally, the performance of the algorithm is judged by the objective evaluation index of the image, the accuracy of crack recognition, and the running time of the algorithm. The image filtering algorithm proposed in this paper can better preserve the edges of the image while enhancing the image. The objective evaluation indexes of the image have been improved significantly, and the main body of the crack can be accurately identified. The overall crack recognition accuracy rate can reach 97.5%, which is higher than the existing tunnel lining crack recognition algorithm, and the average calculation time for each image is shorter. This algorithm has high research significance and engineering application value.
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1 School of Civil Engineering and Architecture, Shandong University of Science and Technology, Qingdao 266590, China; Shandong Civil Engineering Disaster Prevention and Mitigation Laboratory, Shandong University of Science and Technology, Qingdao 266590, China
2 School of Civil Engineering and Architecture, Shandong University of Science and Technology, Qingdao 266590, China