Research on bridge surface defects based on YOLOv7

Abstract

This paper explores deep learning methods for bridge surface defect detection, particularly an improved model based on YOLOv7. To address the detection of bridge-type defects, a Dual-Stream Attention Module (DSAM) and a Hybrid Atrous Pyramid Module (DP) were introduced, aiming to enhance the model's capability to capture key features and the efficiency of multi-scale feature extraction. Experimental results show that the improved model demonstrates higher detection accuracy on a bridge defect dataset, with the YOLOv7+DSAM+DP model achieving a mean Average Precision (mAP) of 91.3%. Additionally, the study compares the SSD and Faster R-CNN networks, confirming the superiority of the proposed model. Overall, the model presented in this paper significantly improves the precision and efficiency of bridge surface defect detection by integrating attention mechanisms and hybrid atrous pyramid modules.