Open Access

ARTICLE

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

Zhengji Li¹, Fazhan Xiong¹, Boyun Huang¹, Meihui Li¹, Xi Xiao², Yingrui Ji^3,4, Jiacheng Xie^1,2, Aokun Liang⁵, Hao Xu^6,*

1 School of Computer and Software, Chengdu Jincheng College, Chengdu, 611731, China
2 College of Arts and Sciences, University of Alabama at Birmingham, Birmingham, AL 35294, USA
3 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100193, China
4 School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100193, China
5 School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
6 Department of Medicine, Harvard Medical School, Boston, MA 02115, USA

* Corresponding Author: Hao Xu. Email:

(This article belongs to the Special Issue: Attention Mechanism-based Complex System Pattern Intelligent Recognition and Accurate Prediction)

Computers, Materials & Continua 2025, 84(3), 5613-5635. https://doi.org/10.32604/cmc.2025.066188

Received 01 April 2025; Accepted 05 June 2025; Issue published 30 July 2025

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.

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Keywords

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