Improvement in Pavement Defect Scenarios Using an Improved YOLOv10 with ECA Attention, RefConv and WIoU

Abstract

This study addresses challenges such as multi-scale defects, varying lighting, and irregular shapes by proposing an improved YOLOv10 model that integrates the ECA attention mechanism, RefConv feature enhancement module, and WIoU loss function for complex pavement defect detection. The RefConv dual-branch structure achieves feature complementarity between local details and global context (mAP increased by 2.1%), the ECA mechanism models channel relationships using 1D convolution (small-object recall rate increased by 27%), and the WIoU loss optimizes difficult sample regression through a dynamic weighting mechanism (location accuracy improved by 37%). Experiments show that on a dataset constructed from 23,949 high-resolution images, the improved model’s mAP reaches 68.2%, which is an increase of 6.2% compared to the baseline YOLOv10, maintaining a stable recall rate of 83.5% in highly reflective and low-light scenarios, with an inference speed of 158 FPS (RTX 4080), providing a high-precision real-time solution for intelligent road inspection.