A RevVIT-based discrimination model for concrete crack images

JiaWen Zhao

doi:10.1117/12.3058011

3 January 2025 A RevVIT-based discrimination model for concrete crack images

JiaWen Zhao

Author Affiliations +

Proceedings Volume 13519, Third International Conference on Communications, Information System, and Data Science (CISDS 2024); 135190J (2025) https://doi.org/10.1117/12.3058011
Event: Third International Conference on Communications, Information System and Data Science 2024, 2024, Nanjing, China

Abstract

Cracks in concrete are a major hazard to the safety and durability of buildings. Efficient detection and timely repair of these cracks have become pressing issues in the field of civil engineering. Existing research suffers from shortcomings such as insufficient data, difficulties in feature extraction, and an imbalance between accuracy and computational cost, hindering the practical application of models. This paper presents an automated crack detection model based on the Revisiting Vision Transformer (RevVIT), with deep learning optimizations tailored to the complexity and diversity of crack images. Various data augmentation techniques were applied to preprocess raw images from highways, bridges, dams, and other structures to create a high-quality crack dataset. The RevVIT model was then introduced to address the inadequacies of traditional models in feature extraction under complex environments, and its performance was compared against VGG19 and three other baseline models on this dataset. Experimental results show that the RevVIT model achieved a classification accuracy of 99.03% in crack detection tasks, demonstrating high robustness and significantly outperforming existing methods, while also delivering superior efficiency in training and inference times.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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JiaWen Zhao "A RevVIT-based discrimination model for concrete crack images", Proc. SPIE 13519, Third International Conference on Communications, Information System, and Data Science (CISDS 2024), 135190J (3 January 2025); https://doi.org/10.1117/12.3058011

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KEYWORDS

Performance modeling

Data modeling

Deep learning

Education and training

Feature extraction

Transformers

Image processing

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