MGD-YOLO: An Enhanced Road Defect Detection Algorithm Based on Multi-Scale Attention Feature Fusion

Accurate and real-time road defect detection is essential for ensuring traffic safety and infrastructure maintenance. However, existing vision-based methods often struggle with small, sparse, and low-resolution defects under complex road conditions. To address these limitations, we propose Multi-Scale Guided Detection YOLO (MGD-YOLO), a novel lightweight and high-performance object detector built upon You Only Look Once Version 5 (YOLOv5). The proposed model integrates three key components: (1) a Multi-Scale Dilated Attention (MSDA) module to enhance semantic feature extraction across varying receptive fields; (2) Depthwise Separable Convolution (DSC) to reduce computational cost and improve model generalization; and (3) a Visual Global Attention Upsampling (VGAU) module that leverages high-level contextual information to refine low-level features for precise localization. Extensive experiments on three public road defect benchmarks demonstrate that MGD-YOLO outperforms state-of-the-art models in both detection accuracy and efficiency. Notably, our model achieves 87.9% accuracy in crack detection, 88.3% overall precision on TD-RD dataset, while maintaining fast inference speed and a compact architecture. These results highlight the potential of MGD-YOLO for deployment in real-time, resource-constrained scenarios, paving the way for practical and scalable intelligent road maintenance systems.