A Dual Deep Network Based on the Improved YOLO for Fast Bridge Surface Defect Detection

Surface defect detection is a critical step to ensure bridge safety. However, there are various types of bridge surface defects, different defects have a wide range of variation in appearance and generally overlap with each other. The existing algorithms cannot efficiently and precisely detect such defects. To solve this problem, we improve the YOLO (You only look once) to enhance the performance of the network to detect multiple defects, YOLO-lump and YOLO-crack are proposed to form a dual deep network for fast bridge surface defect detection. On the one hand, the YOLO-lump can realize the detection of the lump defects on larger sub-images, by employing a hybrid dilated pyramid module based on the hybrid dilated convolution and the spatial pyramid pooling to extract multi-scale features and to avoid losing local information caused by the dilated convolution. On the other hand, the YOLO-crack can realize the detection of the crack defects on smaller sub-images, by proposing a downsampling attention module which uses the 1×1 convolution and the 3×3 group convolution to respectively map cross-channel correlation and spatial correlation of features, enhancing the foreground response of the crack in the downsampling stage and reducing the loss of spatial information. Experimental results show that the proposed algorithm can improve the detection accuracy of the bridge surface defects and realize real-time detection. Copyright ©2022 Acta Automatica Sinica. All rights reserved.