MADF-YOLOv8: A Lightweight Model for Road Distress Detection Based on Adaptive Multiscale Feature Fusion

© 2025 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

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Abstract

Efficient road distress detection is crucial for transportation safety. To address the challenge of balancing detection accuracy, efficiency, and multi-scale feature fusion in existing methods, this paper proposes a lightweight model named MADF-YOLOv8. The model enhances multi-scale feature extraction capability by introducing the Multi-Scale Ghost Residual Convolution (MSGRConv) and the Multiscale Adaptive Feature Processing Module (MAFP). Furthermore, it constructs a Multi-scale Dynamic sampling Bidirectional Feature Pyramid Network (MD-BiFPN) and incorporates the C2f-Faster module to optimize feature fusion efficiency. Experiments on the RDD2022 dataset demonstrate that the proposed model achieves a mean Average Precision at 0.5 Intersection over Union (mAP@0.5) of 88.6% with only 2.312 million parameters. Its overall performance surpasses various mainstream detectors, achieving an exceptional balance between accuracy and efficiency.

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