基于改进YOLO的双网络桥梁表观病害快速检测算法

桥梁表观病害检测是确保桥梁安全的关键步骤. 然而, 桥梁表观病害类型多样, 不同病害间外观差异显著且病害之间可能发生重叠, 现有算法无法实现快速且准确的桥梁多病害检测. 针对这一问题, 对YOLO (You only look once) 进行了改进, 提出了YOLO-lump和YOLO-crack以提高网络检测多病害的能力, 进而形成基于双网络的桥梁表观病害快速检测算法. 一方面, YOLO-lump在较大的滑动窗口图像上实现块状病害的检测. 在YOLO-lump中, 提出了混合空洞金字塔模块, 其结合了混合空洞卷积与空间金字塔池化, 用于提取稀疏表达的多尺度特征, 同时可以避免空洞卷积造成的局部信息丢失; 另一方面, YOLO-crack在较小的滑动窗口图像上实现裂缝病害的检测. 在YOLO-crack中, 提出了下采样注意力模块, 利用1×1卷积和3×3分组卷积分别解耦特征的通道相关性和空间相关性, 可以增强裂缝在下采样阶段的前景响应, 减少空间信息的损失. 实验结果表明, 该算法能够提高桥梁表观病害检测的精度, 同时可实现病害的实时检测.

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.