Research on pavement crack detection algorithm based on improved Yolov5

Tao Ma; Liucun Zhu; Xiao Wu

doi:10.1117/12.3029691

21 June 2024 Research on pavement crack detection algorithm based on improved Yolov5

Tao Ma, Liucun Zhu, Xiao Wu

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131671H (2024) https://doi.org/10.1117/12.3029691
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

In order to efficiently and accurately identify and detect pavement cracks, this study proposes a pavement crack detection algorithm based on an improved Yolov5s network model. The algorithm introduces a weighted bi-directional feature pyramid network BiFPN as a neck feature network for fusing feature maps of different dimensions, thus enhancing the bottom feature information and improving the feature aggregation effect. In addition, CBAM, an attention mechanism, is employed to enhance the learning and extraction of feature information from the pavement crack image, while attenuating the influence of the pavement background, which is similar to the crack, on the detection results. The experimental results on the homemade dataset show that the improved yolov5s model improves 10%, 2.2%, and 4.6% over the original model in terms of precision, recall, and mean average precision values, respectively. This indicates that the improved algorithm is feasible for pavement crack inspection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tao Ma, Liucun Zhu, and Xiao Wu "Research on pavement crack detection algorithm based on improved Yolov5", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131671H (21 June 2024); https://doi.org/10.1117/12.3029691

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KEYWORDS

Detection and tracking algorithms

Feature fusion

Roads

Deep learning

Machine learning

Data modeling

Education and training

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