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.

翻译：深度学习在裂缝分割中发挥重要作用，但现有工作大多采用通用或改进模型，未针对该任务进行专门开发。对物体位置和细节敏感的高分辨率卷积神经网络有助于提升裂缝分割性能，却与实时检测需求存在矛盾。本文提出面向裂缝分割的语义引导高分辨率网络HrSegNet，在保证实时推理速度的同时保留裂缝细节。经复合数据集CrackSeg9k及场景专用数据集Asphalt3k、Concrete3k的评估，HrSegNet的分割性能达到最优水平，其效率远超对比模型。该方法揭示了高分辨率建模与实时检测之间的权衡关系，为边缘设备在实际场景中的裂缝分析应用提供了支撑。