Research on Pavement Crack Identification Method Based on Improved YOLOv11

Pavement cracks are the most predominant form of distress in asphalt pavements; therefore, pavement crack detection constitutes a critical component of pavement maintenance. To address the issues of low recognition accuracy, false detection, and missed detection in highway pavement crack identification, this paper proposes a pavement crack detection model based on YOLOv11.First, Ghost convolution and DynamicConv are integrated into the C3K2 module to reduce computational load and enhance feature extraction capabilities. Second, the CGA attention mechanism is introduced in combination with C2PSA to strengthen feature fusion and contextual information extraction. Finally, the Adaptive Threshold Focal Loss function is employed to address the issue of class imbalance and improve the detection capability for difficult samples. Experiments conducted on a self-constructed dataset demonstrate that the improved model outperforms existing methods in both accuracy and efficiency. The proposed method provides an efficient and accurate solution for pavement crack detection.