Cracking is a common pavement distress that would cause further severe problems if not repaired timely, which means that it is important to accurately extract the information of pavement cracks through detection and segmentation. Automated pavement crack detection and segmentation using deep learning are more efficient and accurate than conventional methods, which could be further improved. While many existing studies have utilized deep learning in pavement crack segmentation, which segments cracks from non‐crack regions, few studies have taken the exact pavement crack detection into account, which identifies cracks in the images from other objects. A two‐step pavement crack detection and segmentation method based on convolutional neural network was proposed in this paper. An automated pavement crack detection algorithm was developed using the modified You Only Look Once 3rd version in the first step. The proposed crack segmentation method in the second step was based on the modified U‐Net, whose encoder was replaced with a pre‐trained ResNet‐34 and the up‐sample part was added with spatial and channel squeeze and excitation (SCSE) modules. Proposed method combines pavement crack detection and segmentation together, so that the detected cracks from the first step are segmented in the second step to improve the accuracy. A dataset of pavement crack images in different circumstances were also built for the study. The F1 score of proposed crack detection and segmentation methods are 90.58% and 95.75%, respectively, which are higher than other state‐of‐the‐art methods. Compared with existing one‐step pavement crack detection or segmentation methods, proposed two‐step method showed advantages of accuracy.

中文翻译：

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基于两步卷积神经网络的自动路面裂缝检测与分割

裂缝是常见的路面困扰，如果不及时修复会引起进一步的严重问题，这意味着通过检测和分割准确提取路面裂缝信息非常重要。与传统方法相比，使用深度学习进行自动路面裂缝检测和分割更加有效和准确，可以进一步加以改进。尽管许多现有研究已在路面裂缝分割中利用深度学习来对非裂缝区域的裂缝进行分割，但很少有研究将确切的路面裂缝检测纳入考量，从而识别出其他物体图像中的裂缝。提出了一种基于卷积神经网络的两步路面裂缝检测与分割方法。第一步，使用修改后的You Only Look Once 3rd版本开发了一种自动路面裂缝检测算法。第二步中建议的裂缝分割方法基于改进的U-Net，其编码器替换为预训练的ResNet-34，上采样部分添加了空间和通道压缩与激励（SCSE）模块。提出的方法将路面裂缝检测和分割结合在一起，以便在第二步骤中对第一步检测到的裂缝进行分割，以提高精度。还为研究建立了不同情况下的路面裂缝图像数据集。提议的裂纹检测和分割方法的F1分数分别为90.58％和95.75％，高于其他最新方法。