Abstract
Automated road damage detection has become a critical component of intelligent transportation systems, enabling timely infrastructure maintenance and enhanced traffic safety. However, detecting pavement defects such as cracks, potholes, and surface degradation remains challenging due to significant scale variation, irregular geometries, illumination changes, and class imbalance. This study proposes a real-time Multi-Scale Feature Pyramid YOLO architecture designed to achieve accurate and deployment-efficient multi-class road damage detection. The framework integrates hierarchical feature extraction with bidirectional multi-scale fusion to enhance sensitivity to both small and large defects. A decoupled detection head is employed to improve classification–localization balance, while focal loss and small-object emphasis mechanisms address class imbalance and fine-grained crack detection challenges. Comprehensive experiments conducted on a multi-class road damage dataset demonstrate that the proposed model achieves a mAP@0.5 of 0.68 and a recall of 0.81, outperforming several representative real-time detection approaches. Precision–recall analysis, confusion matrix evaluation, and ablation studies confirm the effectiveness of multi-scale feature aggregation and targeted optimization strategies. Qualitative results further illustrate robust detection performance under diverse environmental conditions. The proposed framework provides a practical trade-off between accuracy and computational efficiency, making it suitable for real-world deployment in intelligent road condition monitoring systems.