A YOLOv8-Based Model for Small-Target Road Defect Detection

Accurate detection of small road defects in complex backgrounds remains challenging due to limited target features and substantial background interference. To improve detection accuracy and model robustness, this study proposes an enhanced road defect detection framework integrating a multi-attention detection head, a structurally re-parameterized Visual Geometry Group (VGG) architecture, and Masked Generative Distillation (MGD). The multi-attention detection head enlarges the receptive field and enhances the model’s ability to capture and distinguish small-target features in complex scenes. The structurally re-parameterized VGG architecture simplifies the network structure while improving detection accuracy. In addition, MGD is employed to transfer discriminative knowledge from a teacher model to a student model, thereby strengthening feature representation and generalization capability. Experiments on the officially released Road Defect Detection 2022 (RDD2022) dataset show that the proposed model improves mean average precision (mAP) by 3.4% over the baseline model. On the Street View Image Dataset for Automated Road Damage Detection (SVRDD), the model achieves a 5.9% mAP improvement. These results demonstrate that the proposed framework effectively improves road defect detection performance across both datasets and provides a feasible approach for robust detection in complex road environments.