An advanced method for surface damage detection of concrete structures in low-light environments based on image enhancement and object detection networks

Abstract Surface damage detection in concrete structures is critical for maintaining structural integrity, yet current object detection algorithms often struggle in low-light environments. To address this challenge, this study proposed a methodology that integrates image enhancement and object detection networks to improve damage identification in such conditions. Specifically, we employ the self-calibrated illumination (SCI) model to reconstruct low-light images, which are then processed by an improved YOLOv5-based network, YOLOv5-GAM-ASFF, incorporating a global attention mechanism (GAM) and adaptive spatial feature fusion (ASFF). The performance of YOLOv5-GAM-ASFF is evaluated on a dataset of concrete structure damage images, demonstrating its superiority over YOLOv5s, YOLOv6s, and YOLOv7-tiny. The results show that YOLOv5-GAM-ASFF achieves a mAP@0.5 of 79.1%, surpassing the other models by 1.3%, 3.3%, and 5.8%, respectively. This approach provides a reliable solution for surface damage detection in low-light environments, advancing the field of structural health monitoring by improving detection accuracy under challenging conditions.