Road Crack Defect Detection Based on Improved YOLOv5

The presence of road defects poses a significant threat to traffic safety, underscoring the critical importance of road defect detection for effective road maintenance. Traditional manual detection methods are characterized by low efficiency, high subjectivity, and hinder subsequent road maintenance efforts. In recent years, with the rapid advancement of computer technology, particularly the development of deep learning techniques, convolutional neural networks have played an increasingly prominent role in road defect detection. Addressing existing issues such as slow detection speed and the complexity of relevant procedures in road surface defect detection methods, this paper proposes a lightweight road defect detection algorithm based on an improved YOLOv5. The algorithm is implemented using Python in the PyCharm development environment, resulting in the creation of a road defect detection software. This software significantly reduces the barriers to entry for road surface defect detection technology, enabling relevant personnel to conduct rapid and efficient road defect detection.