TY - JOUR
T1 - An advanced method for surface damage detection of concrete structures in low-light environments based on image enhancement and object detection networks
AU - Jiang, Tianyong
AU - Liu, Lin
AU - Hu, Chunjun
AU - Li, Lingyun
AU - Zheng, Jianhua
PY - 2024
Y1 - 2024
N2 - 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 [email protected] 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.
AB - 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 [email protected] 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.
KW - Attention mechanism
KW - Concrete structures
KW - Image enhancement
KW - Low-light environments
KW - Surface damage detection
UR - http://www.scopus.com/inward/record.url?scp=85211932531&partnerID=8YFLogxK
U2 - 10.1186/s43251-024-00145-1
DO - 10.1186/s43251-024-00145-1
M3 - Article
AN - SCOPUS:85211932531
SN - 2662-5407
VL - 5
JO - Advances in Bridge Engineering
JF - Advances in Bridge Engineering
IS - 1
M1 - 33
ER -