Abstract This research develops a novel computer vision approach named a spatial-channel hierarchical network (SCHNet), which is feasible to support the automated and reliable concrete crack segmentation at the pixel level. Specifically, SCHNet with a base net Visual Geometry Group 19 (VGG19) contains a self-attention mechanism, which is realized by three parallel modules, including the feature pyramid attention module, the spatial attention module, and the channel attention module. It can not only consider the semantic interdependencies in spatial and channel dimensions, but also adaptively integrate local features into their global dependencies. The segmentation performance is evaluated by a metric named Mean Intersection over Union (IoU) in a public dataset containing 11,000 cracked and non-cracked images with a unified resolution at 256 × 256 pixels (px). The experimental results confirm the effectiveness of the three attention modules, since they can individually increase Mean IoU by 1.62% (74.16%–72.54%), 5.15% (79.31%–74.16%), and 5.76% (79.92%–74.16%), respectively. With the help of new strategies like the data augmentation and multi-grid method, SCHNet can boost Mean IoU to 85.31%. In a comparison of the state-of-the-art models (i.e. U-net, DeepLab-v2, PSPNet, Ding, Dilated FCN) on the test dataset, SCHNet can outperform others with an improvement of at least 7.51% in Mean IoU. Moreover, SCHNet is robust to noises with a better generalization ability under various conditions, including shadows, roughness surfaces, and holes. Overall, this research contributes to developing SCHNet to integrate spatial and channel information in feature extraction, resulting in a more accurate and efficient crack detection process.

中文翻译：

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用于像素级自动裂纹检测的空间通道分层深度学习网络

摘要 本研究开发了一种名为空间通道分层网络 (SCHNet) 的新型计算机视觉方法，该方法可用于支持像素级自动化且可靠的混凝土裂缝分割。具体来说，带有基础网络 Visual Geometry Group 19 (VGG19) 的 SCHNet 包含自注意力机制，该机制由三个并行模块实现，包括特征金字塔注意力模块、空间注意力模块和通道注意力模块。它不仅可以考虑空间和通道维度上的语义相互依赖性，还可以自适应地将局部特征整合到它们的全局依赖性中。分割性能由一个名为 Mean Intersection over Union (IoU) 的指标在包含 11 个的公共数据集中进行评估，000 张裂纹和非裂纹图像，统一分辨率为 256 × 256 像素 (px)。实验结果证实了三个注意力模块的有效性，因为它们可以分别将平均 IoU 提高 1.62% (74.16%–72.54%)、5.15% (79.31%–74.16%) 和 5.76% (79.92%–74.16%) ， 分别。借助数据增强和多网格方法等新策略，SCHNet 可以将平均 IoU 提升至 85.31%。在测试数据集上的最先进模型（即 U-net、DeepLab-v2、PSPNet、Ding、Dilated FCN）的比较中，SCHNet 的平均 IoU 提高至少 7.51%，优于其他模型. 此外，SCHNet 对噪声具有鲁棒性，在各种条件下具有更好的泛化能力，包括阴影、粗糙表面和孔洞。全面的，