YOLOv8-GRW:A YOLOv8-based Algorithm for Road Defect Detection

Abstract

Given the critical importance of road defect detection for ensuring vehicular safety and the inefficiencies and high costs associated with traditional detection methods, this paper introduces an enhanced road defect detection algorithm based on an improved YOLOv8-GRW model. This model incorporates a novel convolutional module, GSPConv, which utilizes GhostConv and space-to-depth (SPD) modifications to replace standard convolutional layers in the YOLOv8 backbone network, thereby significantly enhancing detection accuracy. Additionally, the feature fusion approach employs an optimized RepGFPN method, modified via GhostConv, which reduces the computational and operational load of RepGFPN while improving the model's feature fusion capabilities. Furthermore, the loss function has been designed around the WNIoU loss, which integrates the Normalized Wasserstein Distance (NWD) into the existing WIoU loss to balance the regression of bounding boxes between high and low-quality sample data, enhancing the detection performance for relatively small defects. Experimental results demonstrate a marked improvement in the performance of the modified algorithm over the original YOLOv8 model. Specifically, the detection accuracy rate of the revised algorithm increased by 3.6%, the F1 score increased by 2.2%, and the mAP@0.5 increased by 2.6% . These advancements substantiate the significant enhancements achieved by the proposed algorithm in the application of road defect detection.