Abstract
Concrete pavement cracks can reduce road safety and lead to traffic accidents. Detecting road cracks and implementing appropriate maintenance measures holds significant practical importance. Addressing the low detection accuracy of existing research methods for road crack detection and the limited studies on classifying and rating the severity of road cracks, this study first proposes a COTECANet model based on the ResNet50 architecture. This model effectively detects pavement cracks with a recognition accuracy of 99.8%, surpassing other compared deep learning models. Subsequently, for roads with detected cracks, the maximum inscribed circle radius of the crack contours in the images is computed using the bisection method, thereby obtaining the maximum pixel width of the road cracks. Finally, by proportional conversion, the actual width of the measured pavement cracks is obtained, and the damage severity of the road cracks is classified and rated according to relevant standards. This research can help highway management departments implement corresponding maintenance measures based on the actual conditions of road damage, thereby extending the lifespan of roads and possessing practical application significance.