Abstract
In response to the existing challenges associated with manual interpretation, low efficiency, high leakage, and misdetection rates in detecting defects in urban underground drainage pipes, this study presents a defect detection method of drainage pipe based on improved YOLOv5s. The proposed method improves the detection of large target defects and reduces the leakage detection rate by increasing a deep target detection layer. Additionally, the introduction of deformable convolutional networks (DCN) allows for more accurate feature extraction from targets with complex shapes. Furthermore, the loss function is improved by employing MPDIoU as the bounding box loss function, which not only accelerates the convergence speed of bounding boxes but also enhances target recognition accuracy. Experimental results demonstrate that the improved model surpasses the performance of the original YOLOv5s, exhibiting an improvement of 3.8% in accuracy, 1.9% in recall, and 2.1% in average precision. Additionally, the proposed method achieves an impressive inspection speed of up to 54.64 FPS (frames per second), enabling real-time and efficient drain defect detection. This method is highly practical as it provides technical support for the future deployment of CCTV pipeline robots.
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References
Haurum, J.B., Moeslund, T.B.: Sewer-ML: a multi-label sewer defect classification dataset and benchmark. In: IEEE Computer Society. Virtual, Online, United States (2021)
Moradi, S., Zayed, T., Golkhoo, F.: Review on computer aided sewer pipeline defect detection and condition assessment. Infrastructures 4(1) (2019)
Shaohua, D., Xuan, S., Shuyi, X., et al.: Automatic defect identification technology of digital image of pipeline weld. Nat. Gas Ind. B 6(4) (2018)
Hawari, A., Alamin, M., Alkadour, F., et al.: Automated defect detection tool for closed circuit television (CCTV) inspected sewer pipelines. Autom. Constr. 89 (2018)
Huang, Y.L.: Research on pipeline crack defect detection method based on video images. Xi’an University of Technology (2018)
Redmon, J., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517–6525 (2017)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement (2018)
Bochkovskiy, A., Wang, C., Liao, H.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv (2020)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Liu, Y., Zhu, S., Qiu, W., et al.: A lightweight faster R-CNN for ship detection in SAR images. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022)
Yu, W., Ren, Y., Hu, C., et al.: Using the improved mask R-CNN and softer-NMS for target segmentation of remote sensing image. In: Proceedings of 2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI), pp. 1–6 (2021)
Wang, A.M., Lei, B.H., Chen, J.C.P.C.: Towards an automated condition assessment framework of underground sewer pipes based on closed-circuit television (CCTV) images. Tunnel. Underground Space Technol. 110 (2021)
Li, D., Xie, Q., Yu, Z., et al.: Sewer pipe defect detection via deep learning with local and global feature fusion. Autom. Constr. 129(2), 103823 (2021)
Lu, Q.R., Ding, X., Liang, Y.W.: Underground drainage pipe defect recognition algorithm based on improved YOLOX. Electron. Meas. Technol. 45(21), 161–168 (2022)
Dai, J., Qi, H., Xiong, Y., et al.: Deformable convolutional networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 764–773 (2017)
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This research is jointly supported by the National Natural Science Foundation of China (62072414).
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Sun, Y., Zhong, W., Li, Y., Cui, X., Zhao, Z., Chen, W. (2024). A Defect Detection Method of Drainage Pipe Based on Improved YOLOv5s. In: Huang, DS., Premaratne, P., Yuan, C. (eds) Applied Intelligence. ICAI 2023. Communications in Computer and Information Science, vol 2015. Springer, Singapore. https://doi.org/10.1007/978-981-97-0827-7_13
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DOI: https://doi.org/10.1007/978-981-97-0827-7_13
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