Real-time defect detection in underground sewage pipelines using an improved YOLOv5 model
Jingyi Lu, Wenjie Song, Yuxuan Zhang, Xianfei Yin, Shunyi Zhao
Abstract
Sewer systems are critical to smart city infrastructure, but conventional pipeline inspection methods cause high costs and inefficiency. This paper presents a real-time detection method for pipeline defects based on an improved you only look once version 5 (YOLOv5) algorithm. The proposed approach enhances the ability of the network to extract and fuse information by incorporating a selective kernel attention mechanism, a bidirectional cascade feature fusion structure, and an optimized loss function. Experimental results indicate that the proposed method can accurately identify and localize ten common types of defects. It achieves a mean average precision that is 4.5% higher than the original model and a frame rate of 69.9 frames per second, making it highly suitable for automated pipeline defect detection. Lastly, future research directions are outlined, including exploring lightweight architectures and adaptive mechanisms to improve the generalization of model to diverse defect types and environments.