Research on bridge crack detection based on improved UNet

YueJiu Li; ShuiPing Li

doi:10.1117/12.3035761

16 August 2024 Research on bridge crack detection based on improved UNet

YueJiu Li, ShuiPing Li

Proceedings Volume 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024); 1323014 (2024) https://doi.org/10.1117/12.3035761
Event: Third International Conference on Machine Vision, Automatic Identification and Detection, 2024, Kunming, China

Abstract

This paper mainly realizes the crack contour extraction function of bridge surface crack detection system, and constructs the crack semantic segmentation model by deep learning algorithm to realize pixel level binary segmentation of cracks. Based on the Unet model, we added attention mechanism to the skip connection part and constructed a joint loss function in the loss function part. In the set experimental environment, we conducted a comparison experiment with the commonly used semantic segmentation model. The experimental results demonstrate a significant enhancement in segmentation performance achieved by the improved model. The overall accuracy reached 0.9015, recall reached 0.7199, and the F1 score reached 0.9225. All indicators are higher than other models, and the previous problems have been greatly improved. Therefore, this model can be used as the image segmentation model of the crack detection system.

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

Citation Download Citation

YueJiu Li and ShuiPing Li "Research on bridge crack detection based on improved UNet", Proc. SPIE 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024), 1323014 (16 August 2024); https://doi.org/10.1117/12.3035761

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KEYWORDS

Image segmentation

Education and training

Convolution

Data modeling

Semantics

Bridges

Performance modeling

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