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A robust abnormal detection method for complex structures in UAV images for autonomous OM system

机译：自主O＆M系统UAV图像中复杂结构的强大异常检测方法

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Abnormal detection using 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. 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.
机译：使用UAV平台的异常检测变得越来越流行的操作和维护，特别是对于像建筑物，桥梁等的大型建筑，可以预期UAV使用的检测系统可以降低成本，确保安全性并为O＆M提供稳定性基础设施。图像配准和更改检测方法在异常检测系统中起着核心作用。需要改善这两个关键因素。首先，由于近距离拍摄和结构的复杂表面组成，强大的平面级匹配方法对于改变检测来制备高精度的图像配准。然而，正如结构表面的许多部分没有足够的特征点，似乎难以基于对应特征点使用相同转换的平面匹配。其次，由于同性传输偏差和信息冗余，平面变化检测在边界区域中具有很大的噪声。为了解决这两个问题，提出了一种基于边缘检测和几何约束的组合的鲁棒方法，以进行平面级配准和改变检测降噪。对于注册，良好地利用边境区域中的像素信息，我们展开边界区域以提取每个平面，无论特征点的数量如何。对于降噪，我们强调边界信息以降低信息冗余的影响。用几组图像对进行验证实验。我们成功地提取了92％覆盖率和91％精度的图像中的平面，而噪声数量减少为平均水平的30％。评价表明，我们的提出方法具有高精度，具有高稳健性对异常检测系统。

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