Abstract
Aiming at the complex background of transmission lines at the present stage, which leads to the problem of low accuracy of insulator fault detection for small targets, a deep learning-based insulator fault detection algorithm for transmission lines is proposed. First, aerial images of insulators are collected using UAVs in different scenarios to establish insulator fault datasets. After that, in order to improve the detection efficiency of the target detection algorithm, certain improvements are made on the basis of the YOLOV9 algorithm. The improved algorithm enhances the feature extraction capability of the algorithm for insulator faults at a smaller computational cost by adding the GAM attention mechanism; at the same time, in order to realize the detection efficiency of small targets for insulator faults, the generalized efficient layer aggregation network (GELAN) module is improved and a new SC-GELAN module is proposed; the original loss function is replaced by the effective intersection-over-union (EIOU) loss function to minimize the difference between the aspect ratio of the predicted frame and the real frame, thereby accelerating the convergence speed of the model. Finally, the proposed algorithm is trained and tested with other target detection algorithms on the established insulator fault dataset. The experimental results and analysis show that the algorithm in this paper ensures a certain detection speed, while the algorithmic model has a higher detection accuracy, which is more suitable for UAV fault detection of insulators on transmission lines.
Similar content being viewed by others
Availability of data and materials
The data that support the findings of this study are available on request from the corresponding author upon reasonable request.
References
Zhou, X., Lu, Z., Liu, Y., Chen, S.: Development models and key technologies of future grid in China[J]. Zhongguo Dianji Gongcheng Xuebao/Proc. Chin. Soc. Electric. Eng. 34(29), 4999–5008 (2014)
Khalyasmaa, A.I., Uteuliyev, B.A., Tselebrovskii, Y.V.: Methodology for analyzing the technical state and residual life of overhead transmission lines[J]. IEEE Trans. Power Delivery 36(5), 2730–2739 (2020)
Alhassan, A.B., Zhang, X., Shen, H., Xu, H.: Power transmission line inspection robots: a review, trends and challenges for future research[J]. Int. J. Electric. Power Energy Syst. 118, 105862 (2020)
Girshick, R.: Fast R-CNN[C]. In: 2015 IEEE International Conference on Computer Vision (ICCV). 1440–1448 (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.: SSD: single shot multibox detector[C]. In: Proceedings of the European Conference on Computer Vision (ECCV). 21–37 (2016)
Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection[C]. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 779–788 (2016)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger[C]. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 6517–6525 (2017)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement[J]. arXiv preprint arXiv:1804.02767 (2018)
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection[J]. arXiv preprint arXiv:2004.10934 (2020)
Zhao, Q., Ji, T., Liang, S., et al.: Real-time power line segmentation detection based on multi-attention with strong semantic feature extractor[J]. J. Real-Time Image Proc. 20(6), 117 (2023)
Antwi-Bekoe, E., Liu, G., Ainam, J.P., et al.: A deep learning approach for insulator instance segmentation and defect detection[J]. Neural Comput. Appl. 34, 7253–7269 (2022)
Yang, L., Fan, J., Song, S., et al.: A light defect detection algorithm of power insulators from aerial images for power inspection[J]. Neural Comput. Appl. 34, 17951–17961 (2022)
Yuan, J., Zheng, X., Peng, L., Qu, K., Luo, H., Wei, L., Jin, J., Tan, F.: Identification method of typical defects in transmission lines based on YOLOv5 object detection algorithm[J]. Energy Rep. 9, 323–332 (2023)
Ahmed, M.F., Mohanta, J.C., Sanyal, A.: Inspection and identification of transmission line insulator breakdown based on deep learning using aerial images[J]. Electric Power Syst. Res. 211, 108199 (2022)
Li, W., Tong, Q., Gu, J., et al.: A self-adjusting transformer network for detecting transmission line defects[J]. Neural Comput. Appl. 36(9), 4467–4484 (2024)
Lu, L., Chen, Z., Wang, R., et al.: Yolo-inspection: defect detection method for power transmission lines based on enhanced YOLOv5s[J]. J. Real-Time Image Proc. 20(5), 104 (2023)
Zhao, Y., Zheng, Z., Liu, Y.: Survey on computational-intelligence-based UAV path planning[J]. Knowl.-Based Syst. 158, 0950–7051 (2018)
Zhang, S., Gavrilovskaya, N., Said, N.A., et al.: Correction to: a new approach to snow avalanche rescue using UAV pictures based on convolutional neural networks[J]. J. Real-Time Image Proc. 20(5), 84 (2023)
Wan, P., Xu, G., Chen, J., Zhou, Y.: Deep reinforcement learning enabled multi-UAV scheduling for disaster data collection with time-varying value[J]. IEEE Trans. Intell. Transport. Syst. (2024). https://doi.org/10.1109/TITS.2023.3345280
Shan, J., Huang, P., Loong, C.N., Liu, M.: Rapid full-field deformation measurements of tall buildings using UAV videos and deep learning[J]. Eng. Struct. 305, 0141–0296 (2024)
Wang, C., Yeh, I., Liao, H.: YOLOv9: learning what you want to learn using programmable gradient information[J]. arXiv preprint arXiv:2402.13616 (2024)
Liu, Y., Shao, Z., Hoffmann, N.: Global attention mechanism: retain information to enhance channel-spatial interactions[J]. arXiv preprint arXiv:2112.05561 (2021)
Li, J., Wen, Y., He, L.: SCConv: spatial and channel reconstruction convolution for feature redundancy[C]. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 6153–6162 (2023)
Zhang, Y., Ren, F.W., Zhang, Z., Jia, Z., Wang, L., Tan, T.: Focal and efficient IOU loss for accurate bounding box regression[J]. Neurocomputing 506, 146–157 (2022)
Liu, Y., Shao, Z., Teng, Y., Hoffmann, N.: NAM: normalization-based attention module[J]. arXiv preprint arXiv:2111.12419 (2021)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks[C]. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7132–7141 (2018)
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module[C]. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision-ECCV 2018. 3–19 (2018)
Ouyang, D., He, S., Zhan, J., Guo, H., Huang, Z., Luo, M., Zhang, G.: Efficient multi-scale attention module with cross-spatial learning[C]. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 1–5 (2023)
Chollet, F.: Xception: deep learning with depthwise separable convolutions[C]. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1800–1807 (2017)
Li, H., Li, J., Wei, H., Liu, Z., Zhan, Z., Ren, Q.: Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles[J]. arXiv preprint arXiv:2206.02424 (2022)
Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: GhostNet: more features from cheap operations[C]. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 1577–1586 (2019)
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (U1806201), the Shandong Province Natural Science Foundation of China (ZR2022ME194) and the Major Basic Research Project of Shandong Province Natural Science Foundation (ZR2021ZD12).
Author information
Authors and Affiliations
Contributions
H. W.; Data analysis and Writing, Conceptualization. Q. Y.; Conceptualization, Resources. B. Z.; Validation, writing-review and editing. D. G.; Methodology, Visualization. All authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Ethical approval
The type of research in this paper does not involve ethical issues. Informed consent was obtained from all authors for the publication of this article. The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wang, H., Yang, Q., Zhang, B. et al. Deep learning based insulator fault detection algorithm for power transmission lines. J Real-Time Image Proc 21, 115 (2024). https://doi.org/10.1007/s11554-024-01495-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11554-024-01495-9