A data‐driven method for identifying hidden road defects using ground‐penetrating radar based on lightweight YOLOv11‐PME

To address the challenges of low recognition accuracy and high rates of missed and false detections of small targets in ground‐penetrating radar images of road internal defects, this paper proposes a lightweight detection algorithm named YOLOv11 (You Only Look Once)‐PME, based on an improved YOLOv11 architecture. The method introduces innovations at both the network structure and loss function levels: a lightweight CPU convolutional neural network (PP‐LCNet) is adopted as the backbone to enhance feature extraction capability while reducing parameter count; a multi‐scale cross‐reference attention module (MSCRSimAM) is designed to improve perception and fusion of multi‐scale defect features; and the enhanced MPDIoU (EnMPDIoU) loss function is proposed, which incorporates bounding box diagonal constraints and optimizes centre distance weighting to increase sensitivity to the location and shape of defects. Experimental results demonstrate that YOLOv11‐PME achieves a mean average precision (mAP@0.5) of 96.8% on a self‐established road defect dataset, outperforming the original YOLOv11 by 5.3%, while reducing both parameter count and computational cost by 11.5% and 4.8%, respectively. Field detection results show strong agreement with core validation, indicating that the proposed algorithm exhibits high reliability, generalization capability and strong potential for engineering applications.