Deep learning-based method for detection and feature quantification of microscopic cracks on the surface of concrete dams

Abstract

Efficient detection of surface cracks in concrete dams is crucial for maintaining hydraulic engineering and infrastructure. Accurately identifying and diagnosing microscopic cracks in practical engineering remains challenging. An intelligent method based on an improved YOLOv8s-YOLO-DEW combined with Unet is proposed for microscopic crack detection and information quantification. In YOLO-DEW, designing the CSP Bottleneck with Dynamic Snake Convolutions (C2f_DSC) enhances the fusion of features at different scales, Efficient Multi-Scale Attention (EMA) is introduced to focus on the key information of fracture features, and employing WIoUv3 loss function improves crack localization capability. Subsequently, the Unet is constructed to extract morphological features of micro-fractures. The results indicate that the comprehensive performance of the proposed method is optimal, with 83.5 % mAP for YOLO-DEW, while Unet achieving mIoU and mPA of 80.73 % and 86.49 %. Results from UAV field experiments further confirm the method’s effectiveness, which efficiently and accurately detects microscopic cracks on concrete dam faces.