Crack segmentation model based on linearity-sensitive encoder and global attention mechanism

Jiahao Wang; Honglin Xiang; Yang Zhang; Tianbiao Luo

doi:10.1117/12.3040513

10 September 2024 Crack segmentation model based on linearity-sensitive encoder and global attention mechanism

Jiahao Wang, Honglin Xiang, Yang Zhang, Tianbiao Luo

Author Affiliations +

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 1325715 (2024) https://doi.org/10.1117/12.3040513
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

Abstract

Identifying cracks is a core and significant activity, utilized in a wide range of industrial applications. Great progress has been made in the diversity detection of crack identification and crack segmentation. Although the deep learning method has achieved good results in crack identification accuracy and fine granularity, it has entered the bottleneck when facing the complex situation of pixel-level crack detection. Faced with the task of pixel level semantic segmentation, the existing methods are difficult to segment fine-grained information and have low prediction accuracy. To address these challenges, we propose an improved model based on U-Net framework, LSGAU-Net (Snake Global Attention U-Net). The model includes an encoder structure sensitive to linear topology and a feature fusion module based on global attention, which can enhance the learning of the sinuous features of cracks and fuse semantic information at different scales. Compared with some existing methods, our model not only guarantees the prediction accuracy, but also greatly improves the continuity and robustness of the prediction results. Experiments on multiple crack datasets show that our model outperforms existing models. Moreover, we verified the effectiveness of the model modification through a series of ablation experiments.

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

Citation Download Citation

Jiahao Wang, Honglin Xiang, Yang Zhang, and Tianbiao Luo "Crack segmentation model based on linearity-sensitive encoder and global attention mechanism", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 1325715 (10 September 2024); https://doi.org/10.1117/12.3040513

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KEYWORDS

Feature fusion

RGB color model

Semantics

Roads

Data modeling

Design

Image segmentation

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