Improvement of Dam Crack Detection Algorithm for YOLOv9

Abstract

Dams, as crucial water conservancy engineering facilities, play a role in safe guarding people's livelihoods and providing economic benefits. However, due to the impact of natural factors and human activities, dams may develop cracks and other potential safety hazards during operation. Crack detection can identify these potential issues in a timely manner, allowing for appropriate measures to be taken for repair and reinforcement, thereby preventing catastrophic consequences such as dam breaches under extreme weather or geological conditions. In the process of dam crack detection, this paper presents a method, YOLOv9-LAE, which may solve missed or false detections. Firstly, the large separable kernel attention (LSKA) module is introduced, which emphasises positional information while focusing on channel features. Secondly, the SPPFELAN in YOLOV9 is replaced by the AIFI module, as capturing the key information needed in the image will enable the following modules to accurately detect the crack information. Finally, the EIOU to calculate the loss, accelerating training convergence and improving the accuracy of crack detection. The research results indicate that YOLOV9-LAE achieves a precision of 90.7%, the recall rate is 75.1%, with $mAP@0.5$ at 81.5% and $mAP@0.5:0.95$ at 60.6%. Compared to YOLOv9, the precision has improved by 9.9%, the recall has increased by 2%, $mAP@0.5$ has risen by 1.5% and $mAP@0.5:0.95$ has been enhanced by 1.5%.