Bridge crack segmentation algorithm based on the novel full convolutional neural network

Yun Bai; Haobo Wang; Feng Xing; En Lu; Wencong Liu

doi:10.1117/12.3025708

8 April 2024 Bridge crack segmentation algorithm based on the novel full convolutional neural network

Yun Bai, Haobo Wang, Feng Xing, En Lu, Wencong Liu

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130903P (2024) https://doi.org/10.1117/12.3025708
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

Considering the impact of background noise, the traditional bridge crack detection method cannot completely extract the crack features and effectively fuse them, resulting in low crack detection accuracy, false and missed detection. This paper presents a bridge crack detection algorithm based on improved U-Net (I-U-Net) network, specifically designed to address the mentioned challenges. Firstly, the residuals module with bottleneck structure (Res-Bot) is combined with the U-Net model to obtain complete crack information. Secondly, in order to eliminate the interference of background noise, optimized convolutional block attention module (O-CBAM) is introduced, adding dimensionality reduction convolutional layer and dilated convolutional layer, which effectively reduces the number of network parameters while simultaneously improving the feature extraction capabilities of the network. Thirdly, based on the idea of multi-size fusion, different sizes of convolution kernels are selected according to different levels to extract crack information, and the encoder-decoder-multi-size fusion (EDMSF) module is constructed. Finally, the effectiveness of the I-U-Net network is demonstrated through the public bridge crack dataset and the self-made dataset.

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

Citation Download Citation

Yun Bai, Haobo Wang, Feng Xing, En Lu, and Wencong Liu "Bridge crack segmentation algorithm based on the novel full convolutional neural network", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130903P (8 April 2024); https://doi.org/10.1117/12.3025708

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