Crack Detection on Road Surfaces Based on Improved YOLOv8 - Haiyang Wu; Lingyun Kong; Denghui Liu (IEEE Access)
Road defect detection is vital for road maintenance but remains challenging due to the complexity of backgrounds, low resolution, and crack similarity. This paper introduces YOLOv8-VOS(VOS means ‘vanillaNet+ODConv+SEAttention’), an enhanced road crack detection algorithm that incorporates an improved Vanilla Net backbone with Squeeze-and-Excitation (SE) attention and ODConv modules. The loss function is replaced with WIoU to better balance bounding box regression. Experiments on the RDD2022 dataset demonstrate a 2% improvement in average accuracy over the original YOLOv8, achieving 53.7%. The proposed model effectively identifies road cracks in complex traffic backgrounds, contributing to safer and more efficient road maintenance.