@Article{cmc.2025.066188,
AUTHOR = {Zhengji Li, Fazhan Xiong, Boyun Huang, Meihui Li, Xi Xiao, Yingrui Ji, Jiacheng Xie, Aokun Liang, Hao Xu},
TITLE = {MGD-YOLO: An Enhanced Road Defect Detection Algorithm Based on Multi-Scale Attention Feature Fusion},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {84},
YEAR = {2025},
NUMBER = {3},
PAGES = {5613--5635},
URL = {http://www.techscience.com/cmc/v84n3/63183},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2025.066188}
}