Bridge Surface Crack Detection Algorithm Based on YOLOv3 and Attention Mechanism
CAI Fenghuang1,2, ZHANG Yuexin1,2, HUANG Jie1,2
1.College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108; 2.Key Laboratories of Industrial Automation Control Technology and Information Processing of Fujian Province, Fuzhou University, Fuzhou 350108
Abstract To realize fast and accurate detection of bridge surface cracks for the timely repair, a bridge surface crack detection algorithm based on improved YOLOv3 (Crack-YOLO) is proposed. Crack-YOLO is combined with depthwise separable convolutions and attention mechanism to detect bridge surface cracks in real time. The standard convolution of YOLOv3 is replaced with the depthwise separable convolution to reduce the number of network parameters. Moreover, the inverted residual block of MobileNet V2 is introduced to solve the problem of precision decline caused by depthwise separable convolution. In Crack-YOLO, both channel attention and spatial attention of the image are taken into account through the convolution block attention module to learn the feature selectively. The experimental results show that Crack-YOLO detects the cracks on the surface of the bridge in real time. Compared with YOLOv3, Crack-YOLO produces smaller weights and higher detection accuracy at a higher detection speed.
Corresponding Authors:HUANG Jie, Ph.D., professor. His research interests include pa-ttern recognition, intelligent system and multi-agent system.
About author:: CAI Fenghuang, Ph.D., associate profe-ssor. His research interests include power electronics and control, digital-signal-processing-based control applications and pattern recognition.ZHANG Yuexin, master student. His research interests include image processing and pattern recognition.
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2020, Vol. 33 
