Original scientific paper
https://doi.org/10.7307/ptt.v38i4.1128
Automatic Road Damage Detection Based on Improved YOLO11
Siwei WEI
; School of Computer Science, Hubei University of Technology, Wuhan, China; CCCC Second Highway Consultants Company Ltd., Wuhan, China
Yujian PENG
; School of Computer Science, Hubei University of Technology, Wuhan, China
Hongfang LUO
; Engineering and Technology College, Hubei University of Technology, Wuhan, China
*
Chunzhi WANG
; School of Computer Science, Hubei University of Technology, Wuhan, China
* Corresponding author.
Abstract
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.
Keywords
intelligent transportation system; road damage detection; object detection; deep learning
Hrčak ID:
346669
URI
Publication date:
28.4.2026.
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