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Efficient real-time defect detection for spillway tunnel using deep learning

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Abstract

A spillway tunnel eroded by high-speed water for a long time is prone to the rebar-exposed defects. Therefore, regular defect detection is very important for the safety of the hydropower station. The images of spillway tunnel are obtained by erecting scaffolding, and then the defects are manually recognized. This traditional method has some disadvantages such as high risk, inefficiently, time consumption and strong subjectivity. To improve the efficiency of defect detection, a real-time method is proposed for spillway tunnel defect detection (STDD) using deep learning. First, images of a spillway tunnel are collected by an Unmanned Aerial Vehicle (UAV) system and raw images are cropped and labeled to create a dataset of rebar-exposed defects. Then, the lightweight STDD network is developed using separable convolution and asymmetric convolution, and the network is trained and tested on the dataset. To evaluate the performance of STDD network, a comparative experiment is conducted with other networks. The results show that the STDD network has better detection performance. For defect segmentation, the recall, precision, F1 and mean intersection over union (mIoU) are 89.92%, 93.48%, 91.59%, and 91.73%, respectively. The STDD network has 1.7 M parameters, and the average inference time is 14.08 ms. In summary, the proposed STDD network achieves accurate and real-time defect detection for spillway tunnel, which can provide reliable support for the structure safety evaluation.

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Acknowledgements

The work presented in this paper was supported by the National Key R&D Program of China (Grant No.2019YFB1310505); Sichuan Technology Innovation and Entrepreneurship Seedling Project (Grant No.2020JDRC0130); Sichuan Science and Technology Program (Grant No.2020YFSY0062).

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Authors and Affiliations

  1. School of Information Engineering, Southwest University of Science and Technology, Mianyang, China

    Chuncheng Feng, Hua Zhang & Shuang Wang

  2. Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu, China

    Yonglong Li & Haoran Wang

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  1. Chuncheng Feng
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  2. Hua Zhang
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  3. Yonglong Li
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  4. Shuang Wang
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  5. Haoran Wang
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Correspondence to Yonglong Li.

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Feng, C., Zhang, H., Li, Y. et al. Efficient real-time defect detection for spillway tunnel using deep learning. J Real-Time Image Proc 18, 2377–2387 (2021). https://doi.org/10.1007/s11554-021-01130-x

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