APPLICATION OF MACHINE LEARNING FOR OBJECT DETECTION IN OBLIQUE AERIAL IMAGES

Abstract

At the time of continuous development of all technologies, deep machine learning (more precisely, convolutional neural networks), which is one of the branches of artificial intelligence (AI), has found wide application in many fields, including photogrammetry and remote sensing. One of the areas where a lot of research is conducted using these methods is the recognition of objects in aerial and satellite imagery. Through the application of deep learning algorithms and neural networks, it is possible to automate labour-intensive processes. However, while object detection in images using machine learning is popular for natural scenes and in recent years also for nadir aerial and satellite imagery, for aerial oblique imagery at the moment of this research there were relatively few publications on the subject. The challengeable task in object detection is the time-consuming generation of training datasets when access is limited or non-existent. This study proposed the methodology to automate this process with use of existing resources for transferring of references to new databases for training models for detect objects on aerial oblique images. The object detection was performed using the YOLOv3 neural network. Experiment results tested on two datasets have shown that the proposed method could realize the task of object detection in oblique aerial images.