Underwater Surface Defect Recognition of Bridges Based on Fusion of Semantic Segmentation and Three-Dimensional Point Cloud

This study introduces an innovative approach for identifying surface defects in underwater bridge structures through the fusion of deep learning and three-dimensional point cloud. The method employs a U2-Net neural network enhanced with residual U-blocks to effectively capture defect features across scales and merge multiscale underwater image attributes to produce significant probability images for defect detection. By leveraging three-dimensional digital image correlation techniques, the method reconstructs the bridge pier surfaces' physical dimensions from point cloud, enabling precise defect contour and size recognition. The fusion of deep learning's semantic segmentation with the accurate dimensions from point cloud significantly improves defect detection accuracy, achieving pixel accuracies of 0.943 and 0.811 for foreign objects and spalling and exposed rebars, respectively, and an Intersection over Union of 0.733 and 0.411. The method's millimeter-level precision in point cloud reconstruction further allows for detailed defect dimensioning, enhancing both the accuracy and the quantitative measurement capabilities of underwater bridge inspections, and shows promise for future advanced applications in this field.