A research on an improved Unet-based concrete crack detection algorithm
A research on an improved Unet-based concrete crack detection algorithm
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DOI：
10.1177/1475921720940068
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发表时间：
2020-07
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影响因子：
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通讯作者：
Lingxin Zhang;Junkai Shen;Baijie Zhu
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文献类型：
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作者：
Lingxin Zhang;Junkai Shen;Baijie Zhu
裂缝是评价混凝土结构损伤程度的重要指标。然而，传统的裂纹检测算法实现复杂，泛化能力弱。现有的基于深度学习的裂纹检测算法多为窗口级算法，像素精度较低。本文提出了基于深度学习的CrackUnet模型来解决上述问题。首先，从实验室、地震现场和互联网上收集的裂缝图像被手动调整大小、标记和增强，以形成一个数据集（总共1200个子图像，分辨率为256 × 256 × 3）。然后，提出了一种改进的基于unet的像素级自动裂纹检测方法CrackUnet。为了更准确地检测裂纹，采用了一种新的损失函数——广义骰子损失。研究了数据集的大小和模型的深度对训练时间、检测精度和速度的影响。在测试数据集和先前发布的数据集上对所提出的方法进行了评估。在测试数据集上的最高结果分别达到91.45%、88.67%和90.04%，在CrackForest数据集上的最高结果分别达到98.72%、92.84%和95.44%。通过对检测精度、训练时间和数据集信息量的比较，CrackUnet模型优于其他方法。在此基础上，利用6幅具有复杂噪声的图像研究了CrackUnet模型的鲁棒性和泛化性。
Crack is an important indicator for evaluating the damage level of concrete structures. However, traditional crack detection algorithms have complex implementation and weak generalization. The existing crack detection algorithms based on deep learning are mostly window-level algorithms with low pixel precision. In this article, the CrackUnet model based on deep learning is proposed to solve the above problems. First, crack images collected from the lab, earthquake sites, and the Internet are resized, labeled manually, and augmented to make a dataset (1200 subimages with 256 × 256 × 3 resolutions in total). Then, an improved Unet-based method called CrackUnet is proposed for automated pixel-level crack detection. A new loss function named generalized dice loss is adopted to detect cracks more accurately. How the size of the dataset and the depth of the model affect the training time, detecting accuracy, and speed is researched. The proposed methods are evaluated on the test dataset and a previously published dataset. The highest results can reach 91.45%, 88.67%, and 90.04% on test dataset and 98.72%, 92.84%, and 95.44% on CrackForest Dataset for precision, recall, and F1 score, respectively. By comparing the detecting accuracy, the training time, and the information of datasets, CrackUnet model outperform than other methods. Furthermore, six images with complicated noise are used to investigate the robustness and generalization of CrackUnet models.