Automatic Road Damage Detection Based on Improved YOLO11
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Road damage detection is vital for effective road maintenance and ensuring traffic safety. However, existing object detection models struggle with small objects, interference from complex backgrounds and difficulty handling multi-scale object features. To tackle these challenges, this study proposes an improved road damage detection model based on YOLO11. A novel RoadRep-C3 module is introduced to improve feature extraction, while an efficient multi-scale attention (EMA) mechanism captures multi-scale damage features more effectively. Additionally, a hypergraph structure is incorporated into the neck network to enable cross-stage information fusion, improving the detection of small objects. The proposed model also utilises a slide loss function to optimise performance on challenging samples. Experimental results on the RDD2022 dataset show a 2% increase in mean average precision (mAP@0.5) over the original YOLO11, with a reduced model size. These findings demonstrate the model’s high accuracy and efficiency, offering a practical solution for detecting road damage and enhancing traffic safety.
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Copyright (c) 2026 Siwei WEI, Yujian PENG, Hongfang LUO, Chunzhi WANG
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