A UAV-based machine vision method for bridge crack recognition and width quantification through hybrid feature learning

Bridge crack width is an important indicator to assess and evaluate the health condition of the bridge. In this paper, we have proposed a UAV-based machine vision method for bridge crack recognition and width quantification through hybrid feature learning. Firstly, we have configured a UAV system that can obtain bridge crack image (effective pixel 7952x5304) and GPS position, calculate image resolution, and correct measured plane simultaneously. Then, the crack recognition method combining the R-FCN network and Haar-AdaBoost suited for UAV imagery recognition is proposed, which can make full use of advanced features, shape features and gray features of bridge cracks. The time cost of our method is about 0.2 s for crack detection per one 7952 x 5304 pixels images and 4.5 s in pixel-level segmentation per one 1000 x 1000 pixels bounding box. Additionally, the real bridge crack widths are calculated and quantified by the ranging method using corresponding object distance data. Finally, a case study of the XiangjiangRiver bridge inspection is carried out to demonstrate the effectiveness of the proposed method, achieving above 90% precision in the real bridge crack width quantification. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

An UAV-based machine vision method for bridge crack recognition and width quantification is proposed in this paper. The method combines R-FCN network and Haar-AdaBoost for effective crack identification and quantification of crack width using object distance data. The proposed method achieves over 90% precision in real bridge crack width quantification, as demonstrated in a case study of Xiangjiang River bridge inspection.