Periodic road crack monitoring is an essential procedure for effective pavement management. Highly efficient and accurate crack measurements are key research topics in both academia and industry. Automatic methods gradually replaced traditional manual surveys for more reliable evaluation outputs and better efficiency, whereas the devices are not available to all functional classes of pavements and different departments considering the high cost versus the limited budget. Recently, the widespread use of smartphones and digital cameras made it possible to collect pavement surface crack images at an affordable price in easier ways. However, the qualities of these crack images are diversely influenced by the noises from pavement background, roadways, and so forth. Thus, traditional methods usually fail to extract accurate crack information from pavement images. Therefore, this research proposes a state‐of‐the‐art pixelwise crack detection architecture called CrackU‐net, which is featured by its utilization of advanced deep convolutional neural network technology. CrackU‐net achieved pixelwise crack detection through convolution, pooling, transpose convolution, and concatenation operations, forming the “U”‐shaped model architecture. The model is trained and validated by 3,000 pavement crack images, in which 2,400 for training and 600 for validating, using the Adam algorithm. CrackU‐net has the performance of loss = 0.025, accuracy = 0.9901, precision = 0.9856, recall = 0.9798, and F‐measure = 0.9842 with learning rate of 10⁻². Meanwhile, the false‐positive crack detection problem is avoided in CrackU‐net. Therefore, CrackU‐net outperforms both traditional approaches and fully convolutional network (FCN) and U‐net for pixelwise crack detections.

中文翻译：

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CrackU-net：一种新颖的深度卷积神经网络，用于逐像素路面裂缝检测

定期的道路裂缝监测是有效管理路面的基本程序。高效和准确的裂纹测量是学术界和工业界的关键研究主题。自动方法逐渐取代了传统的手动勘测，以提供更可靠的评估结果和更高的效率，而考虑到高成本和有限的预算，该设备并非适用于所有功能类别的人行道和不同部门。最近，智能手机和数码相机的广泛使用使人们能够以更容易的方式以可承受的价格收集路面裂缝图像。但是，这些裂缝图像的质量受到路面背景，道路等产生的噪声的不同影响。从而，传统方法通常无法从路面图像中提取准确的裂缝信息。因此，本研究提出了一种称为CrackU-net的最先进的像素级裂缝检测架构，该架构的特点是利用了先进的深度卷积神经网络技术。CrackUnet通过卷积，池化，转置卷积和串联操作实现了像素级裂缝检测，从而形成了“ U”形模型架构。使用Adam算法，通过3,000个路面裂缝图像对模型进行训练和验证，其中2,400个用于训练，600个用于验证。CrackU-net的损失率为0.025，精度= 0.9901，精度= 0.9856，召回率= 0.9798，F-measure = 0.9842，学习速率为 这项研究提出了一种称为CrackU-net的最先进的像素级裂缝检测架构，该架构的特点是利用了先进的深度卷积神经网络技术。CrackUnet通过卷积，池化，转置卷积和串联操作实现了像素级裂缝检测，从而形成了“ U”形模型架构。使用Adam算法，通过3,000个路面裂缝图像对模型进行训练和验证，其中2,400个用于训练，600个用于验证。CrackU-net的损失率为0.025，精度= 0.9901，精度= 0.9856，召回率= 0.9798，F-measure = 0.9842，学习速率为 这项研究提出了一种称为CrackU-net的最先进的像素级裂缝检测架构，该架构的特点是利用了先进的深度卷积神经网络技术。CrackUnet通过卷积，池化，转置卷积和串联操作实现了像素级裂缝检测，从而形成了“ U”形模型架构。使用Adam算法，通过3,000个路面裂缝图像对模型进行训练和验证，其中2,400个用于训练，600个用于验证。CrackU-net的损失率为0.025，精度= 0.9901，精度= 0.9856，召回率= 0.9798，F-measure = 0.9842，学习速率为 转置卷积和串联操作，形成“ U”形模型架构。使用Adam算法，通过3,000个路面裂缝图像对模型进行训练和验证，其中2,400个用于训练，600个用于验证。CrackU-net的损失率为0.025，精度= 0.9901，精度= 0.9856，召回率= 0.9798，F-measure = 0.9842，学习速率为 转置卷积和串联操作，形成“ U”形模型架构。使用Adam算法，通过3,000个路面裂缝图像对模型进行训练和验证，其中2,400个用于训练，600个用于验证。CrackU-net的损失率为0.025，精度= 0.9901，精度= 0.9856，召回率= 0.9798，F-measure = 0.9842，学习速率为10 ^-2。同时，在CrackUnet中避免了假阳性裂纹检测问题。因此，CrackU-net的性能优于传统方法以及完全卷积网络（FCN）和U-net的逐像素裂缝检测。