BSGAU-Net for pixel-level road crack segmentation

Yingxiang Lu; Guangyuan Zhang; Tingzhi Qiu; Wei Li

doi:10.1117/12.3012422

21 December 2023 BSGAU-Net for pixel-level road crack segmentation

Yingxiang Lu, Guangyuan Zhang, Tingzhi Qiu, Wei Li

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 1297010 (2023) https://doi.org/10.1117/12.3012422
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Road crack detection holds a crucial significance within the realm of transportation infrastructure management. Its role is instrumental in the preservation and upkeep of road networks, leading to a mitigation of potential accidents and minimizing vehicular wear and tear. The application of deep learning methods in this domain has yielded certain achievements. However, in complex background environments, existing models struggle to effectively extract crack pixels, and predictions regarding intricate details of road cracks lack precision. Addressing these challenges, we propose BSGAUNet(Bypass Supervision Global Attention U-Net) based on the U-Net framework. Global attention module notably combats the interference of background noise, facilitating the accurate extraction of crack pixels. Additionally, bypass auxiliary supervision module enhances the global perception capability of the encoder, enabling the model to more precisely identify crack pixels. To ascertain the accuracy and generalization capacity of the model, we conducted tests on publicly available datasets. The results demonstrated that our model's performance surpassed that of existing models. Furthermore, ablation experiments were employed to validate the effectiveness of the modules.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yingxiang Lu, Guangyuan Zhang, Tingzhi Qiu, and Wei Li "BSGAU-Net for pixel-level road crack segmentation", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 1297010 (21 December 2023); https://doi.org/10.1117/12.3012422

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