Dam Surface Crack Detection Based on CMAM-YOLOv7

Crack detection on the dam surface is the key link of the safety inspection of the hydropower station, but the complex environment has problems such as large interference and noise, complex and changeable shape and unbalanced pixels, which results in poor crack detection effect on the dam surface. We propose a dam surface crack detection method based on CBAM-YOLO and verify it through multiple scenarios. Using the YOLOv7 model, the attention mechanism SE was added between Backbone and Head, and the CBAM-YOLOv7 model was then used to detect the cracks in the new dataset. Experimental validation analyses were performed using FCN, U-Net, YOLOv7, CBAM-YOLOv7 and the methods presented here on the same datasets. The experimental results show that the two-stage dam surface crack detection method based on CBAM-YOLO v7 can accurately detect the cracks and effectively avoid the negative effects. Our method mAP and F1 method can reach 83.81 % and 79.98%, respectively, which can FPS reach 77.86Hz, which is significantly improved compared with other models.