Deep-Learning- and Unmanned Aerial Vehicle-Based Structural Crack Detection in Concrete

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Abstract
Deep-learning- and unmanned aerial vehicle (UAV)-based methods facilitate structural crack detection for tall structures. However, contemporary datasets are generally established using images taken with handheld or vehicle-mounted cameras. Thus, these images might be different from those taken by UAVs in terms of resolution and lighting conditions. Considering the difficulty and complexity of establishing a crack image dataset, making full use of the current datasets can help reduce the shortage of UAV-based crack image datasets. Therefore, the performance evaluation of existing crack image datasets in training deep neural networks (DNNs) for crack detection in UAV images is essential. In this study, four DNNs were trained with different architectures based on a publicly available dataset and tested using a small UAV-based crack image dataset with 648 +pixel-wise annotated images. These DNNs were first tested using the four indices of precision, recall, mIoU, and F1, and image tests were also conducted for intuitive comparison. Moreover, a field experiment was carried out to verify the performance of the trained DNNs in detecting cracks from raw UAV structural images. The results indicate that the existing dataset can be useful to train DNNs for crack detection from UAV images; the TransUNet achieved the best performance in detecting all kinds of structural cracks.
Funding Information
  • Youth Fund of the National Natural Science Foundation of China (52308332, 2022M712787, 12272334, LGG21E080004, J-202203)
  • China Postdoctoral Science Foundation (52308332, 2022M712787, 12272334, LGG21E080004, J-202203)
  • National Natural Science Foundation of China (52308332, 2022M712787, 12272334, LGG21E080004, J-202203)
  • Zhejiang Provincial Natural Science Foundation of China (52308332, 2022M712787, 12272334, LGG21E080004, J-202203)
  • Scientific Research Foundation of Hangzhou City University (52308332, 2022M712787, 12272334, LGG21E080004, J-202203)