Abnormal detection using cameras in UAV platform become more and more popular for operation and maintenance, in particularly for large-scale constructions like building, bridge etc. UAV-used detection system could be expected to reduce the cost, ensure the safety and provide stability for O&M on infrastructures. As imaging technology, Image registration and change detection method plays a central role in an abnormal detection system. Two key factors in this respect are needed to be improved. Firstly, due to the near-distance photographing and complex surface composition of structures, a robust plane-level matching method is significant to make high-precision image registration for the change detection. However, as many part of the surface of structures do not have enough feature points, it seems difficult to make a plane matching using homography transformation based on the correspondence feature points. Secondly, plane-level change detection have much noise in the border area because of homography transfer deviation and information redundancy. In order to solve these two problems, a robust method based on a combination of edge detection and geometry constraint is proposed to make plane-level registration and change detection noise reduction. For registration, making good use of pixel information in the border area, we expand the border area to extract each plane regardless of the number of feature points. And for noise reduction, we excise the border information to reduce the effect of information redundancy. Validation experiments were performed with several sets of image pairs. We succeed to extract planes in images with a 92% coverage and 91% precision while the number of noise is reduced as 30% as before for average. The evaluation shows that our proposed method is of high precision with high robustness for abnormal detection system.
Yu Zhao and Junichiro Watanabe, "A robust abnormal detection method for complex structures in UAV images for autonomous O and M system," Proc. SPIE 10643, Autonomous Systems: Sensors, Vehicles, Security, and the Internet of Everything, 106430P (Presented at SPIE Defense + Security: April 17, 2018; Published: 10 May 2018); https://doi.org/10.1117/12.2306374.
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