Pavement Crack Detection Based on Star-YOLO11

In response to the challenges in highway pavement distress detection, such as multiple defect categories, difficulties in feature extraction for different damage types, and slow identification speeds, this paper proposes an enhanced pavement crack detection model named Star-YOLO11. This improved algorithm modifies the YOLO11 architecture by substituting the original C3k2 backbone network with a Star-s50 feature extraction network. The enhanced structure adjusts the number of stacked layers in the StarBlock module to optimize detection accuracy and improve model efficiency. To enhance the accuracy of pavement crack detection and improve model efficiency, three key modifications to the YOLO11 architecture are proposed. Firstly, the original C3k2 backbone is replaced with a StarBlock-based structure, forming the Star-s50 feature extraction backbone network. This lightweight redesign reduces computational complexity while maintaining detection precision. Secondly, to address the inefficiency of the original Partial Self-attention (PSA) mechanism in capturing localized crack features, the convolutional prior-aware Channel Prior Convolutional Attention (CPCA) mechanism is integrated into the channel dimension, creating a hybrid CPC-C2PSA attention structure. Thirdly, the original neck structure is upgraded to a Star Multi-Branch Auxiliary Feature Pyramid Network (SMAFPN) based on the Multi-Branch Auxiliary Feature Pyramid Network architecture, which adaptively fuses high-level semantic and low-level spatial information through Star-s50 connections and C3k2 extraction blocks. Additionally, a composite dataset augmentation strategy combining traditional and advanced augmentation techniques is developed. This strategy is validated on a specialized pavement dataset containing five distinct crack categories for comprehensive training and evaluation. Experimental results indicate that the proposed Star-YOLO11 achieves an accuracy of 89.9% (3.5% higher than the baseline), a mean average precision (mAP) of 90.3% (+2.6%), and an F1-score of 85.8% (+0.5%), while reducing the model size by 18.8% and reaching a frame rate of 225.73 frames per second (FPS) for real-time detection. It shows potential for lightweight deployment in pavement crack detection tasks.