Concrete dam damage detection and localisation based on YOLOv5s-HSC and photogrammetric 3D reconstruction

Abstract

This paper presents a system for detecting damages in concrete dams that combines the proposed YOLOv5s-HSC algorithm and a three-dimensional (3D) photogrammetric reconstruction method to accurately identify and locate objects. Since the damages usually have complex background and blurred boundaries, Swin transformer blocks and coordinate attention modules were introduced to improve the ability of feature extraction. The mean average precision (mAP) value of the improved algorithm increased by 3.8% and exhibited a reasonably robust performance for both small objects and a considerable detection effect. Subsequently, to realize the localization of the detected damages and mapped to the corresponding positions, the projecting method was proposed by calculating the intersection of the ray from camera center and 3D photogrammetric reconstruction model generated from the same images as for detection. The results confirmed that the proposed method is appropriate for detecting damages and recording locations for intuitively exhibiting concrete dam damages.