Concrete surface crack detection method based on improved deformable convolution with YOLOv5

Shihao Zhang; Yang Li; Mingzhao Song; Xiao Ma

doi:10.1117/12.3003786

11 October 2023 Concrete surface crack detection method based on improved deformable convolution with YOLOv5

Shihao Zhang, Yang Li, Mingzhao Song, Xiao Ma

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128001X (2023) https://doi.org/10.1117/12.3003786
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

This study ensures the safe operation of bridges by real-time recognition of concrete surface cracks on bridges. Traditional crack recognition methods are slow and have low accuracy. To address this issue, we propose an improved YOLOv5 (You Only Look Once version five) detection model based on Deformable Convolution (DCN), called YOLOv5-D. The baseline YOLOv5 model has been optimized in three aspects: First, deformable convolution replaces part of the standard convolution in the backbone network, improving the model's recognition accuracy. Second, the SE attention mechanism is added to adaptively recalibrate channel-wise feature responses, stimulating information features and thereby enhancing the shared underlying feature map representations. Third, the CIOU_Loss is proposed as the loss function, achieving a collaborative optimization of crack detection speed and accuracy, and meeting the deployment requirements of embedded terminals. The improved YOLOv5-D model achieves an average recognition rate (mAP) of 76.1% and a recognition speed of 54 FPS with a model size of 14.5 MB. Compared to the original YOLOv5 model, the average recognition rate is increased by approximately 5.9%, the parameter quantity is reduced by about 10%, and the detection speed remains essentially unchanged. In summary, the YOLOv5-D model achieves a better balance in terms of accuracy and speed, effectively locates cracks, and provides a new technology for early warning of bridge collapse disasters.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shihao Zhang, Yang Li, Mingzhao Song, and Xiao Ma "Concrete surface crack detection method based on improved deformable convolution with YOLOv5", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128001X (11 October 2023); https://doi.org/10.1117/12.3003786

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KEYWORDS

Convolution

Data modeling

Deformation

Bridges

Machine learning

Detection and tracking algorithms

Education and training

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