Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation

• URL: http://arxiv.org/abs/2307.00270v2
• Date: Mon, 5 Feb 2024 13:21:37 GMT
• Title: Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation
• Authors: Yongshang Li, Ronggui Ma, Han Liu and Gaoli Cheng
• Abstract summary: This paper describes HrSegNet, a high-resolution network with semantic guidance specifically designed for crack segmentation. HrSegNet guarantees real-time inference speed while preserving crack details. This approach demonstrates that there is a trade-off between high-resolution modeling and real-time detection.
• Score: 4.651261550392625
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep learning plays an important role in crack segmentation, but most work utilize off-the-shelf or improved models that have not been specifically developed for this task. High-resolution convolution neural networks that are sensitive to objects' location and detail help improve the performance of crack segmentation, yet conflict with real-time detection. This paper describes HrSegNet, a high-resolution network with semantic guidance specifically designed for crack segmentation, which guarantees real-time inference speed while preserving crack details. After evaluation on the composite dataset CrackSeg9k and the scenario-specific datasets Asphalt3k and Concrete3k, HrSegNet obtains state-of-the-art segmentation performance and efficiencies that far exceed those of the compared models. This approach demonstrates that there is a trade-off between high-resolution modeling and real-time detection, which fosters the use of edge devices to analyze cracks in real-world applications.

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