Abstract:  In response to the inefficiency caused by the reliance on manual measurement in existing road crack classification methods, a road crack detection model is proposed. This model improves detection accuracy by enhancing the detection algorithm and employs a bisection method to precisely measure the actual width of cracks, thereby enabling automatic classification and grading of crack damage levels. Specifically, a COTECANet ( Contextual Transformer Efficient Channel Attention Network ) model based on the ResNet50 architecture is first introduced, which outperforms other compared deep learning models. For the detection results of this model, the maximum inscribed circle radius of the crack contours in the image is calculated using the bisection method for roads with pavement cracks, thereby obtaining the maximum pixel width of the road cracks. The actual width of the pavement cracks can be derived by converting the measurements according to the corresponding scale, and the damage level of the road cracks is classified and graded based on national standards. Experimental results demonstrate that the COTECANet model can effectively detect pavement cracks, achieving an accuracy rate of 99. 8% in road crack identification. The above method provides more scientific and efficient technical support for road maintenance, with significant theoretical and engineering application prospects.

Key words: road cracks, residual neural networks, dichotomy, radius of the largest inscribed circle of the crack, crack classification and evaluation