Road surface crack detection and classification based on YOLOv5

Abstract

The burgeoning need for sustainable urban transportation has magnified the importance of effective road infrastructure maintenance, particularly road crack detection. This study addresses the challenge of accurately detecting road surface cracks through automated methods. To accurately identify road surface cracks, we used the YOLOv5 image recognition algorithm, enhanced by comprehensive data augmentation and meticulous training strategies. The YOLOv5 proved superior to traditional methods in accuracy and recall while maintaining high computational speed and a smaller model size in experimental outcomes. The findings affirm that YOLOv5-based detection technologies significantly improve maintenance efficiency and traffic safety, with future work poised to further refine these approaches for greater robustness and integration into intelligent transportation systems.