Bridge crack image classification based on improved transformer

Dan Tao; Shouhong Guo; Guangying Qiu; Dequan You; Linming Wu

doi:10.1117/12.3054565

20 December 2024 Bridge crack image classification based on improved transformer

Dan Tao, Shouhong Guo, Guangying Qiu, Dequan You, Linming Wu

Author Affiliations +

Proceedings Volume 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024); 134212E (2024) https://doi.org/10.1117/12.3054565
Event: Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 2024, Dalian, China

Abstract

As a major bridge country, China's facilities are vital to the transportation network and regional economic development. However, bridges exposed to the natural environment and traffic pressure for a long time are prone to problems such as cracks and corrosion, which, if not detected and repaired promptly, may lead to structural failures, affecting safety and economic benefits. Traditional image classification methods are susceptible to interference from shadows and dirt in practical applications, leading to reduced classification accuracy and limitations in dealing with fine-grained features and small sample datasets. In this paper, based on the Transformer model, combined with AlignMixup and Omage2Token data enhancement methods, the accuracy and recognition ability of the model on fine-grained and small-sample image classification is improved.

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

Citation Download Citation

Dan Tao, Shouhong Guo, Guangying Qiu, Dequan You, and Linming Wu "Bridge crack image classification based on improved transformer", Proc. SPIE 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 134212E (20 December 2024); https://doi.org/10.1117/12.3054565

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