JERAJournal of Electronic Research and Application2208-35022208-3510Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jera.v9i6.13159ArticleMADF-YOLOv8: A Lightweight Model for Road Distress Detection Based on Adaptive Multiscale Feature Fusionhttps://artdesignp.com/journal/JERA/9/6/10.26689/jera.v9i6.13159OuYangTao,YuHaohui,PanGuanlin,CuiYan,ChangQingling,FuXiulong2025962025-12-16Efficient 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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