Abstract
Multi-object detection for power transmission line is one of the key tasks to control and monitor quality of the system. In the past, defective objects were found out by naked-eye inspection through aerial images and relied on the experienced workers. Due to harsh environmental conditions, manual observation might be a time-consuming and dangerous task. Recently, this task has been supported by machine learning where deep-learning algorithms are applied to increase the efficiency of detection/recognition phases. This paper discusses different approaches for multi-object detection based on Convolutional Neural Network (CNN) model to investigate the quality and condition of power lines in Vietnam. Our proposed system outperforms the state-of-the-art methods on our dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pagnano, A., Höpf, M., Teti, R.: A roadmap for automated power line inspection. Maintenance and repair. Proc. Cirp 12, 234–239 (2013)
Shakhatreh, H., Sawalmeh, A.H., Al-Fuqaha, A., Dou, Z.: Unmanned aerial vehicles (UAVs): a survey on civil applications and key research challenges. IEEE Access 7, 48572–48634 (2019)
Liu, X., Miao, X., Jiang, H., Chen, J.: Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology. Preprint arXiv:2003.09802 (2020)
Zhai, Y., Chen, R., Yang, Q., Li, X., Zhao, Z.: Insulator fault detection based on spatial morphological features of aerial images. IEEE Access 6, 35316–35326 (2018)
Wang, Y.L., Yan, B.: Vision based detection and location for cracked insulator. Comput. Eng. Design 35(2), 583–587 (2014)
Oberweger, M., Wendel, A., Bischof, H.: Visual recognition and fault detection for power line insulators. In: Computer Vision Winter Workshop (2014)
Hao-ran, J., Lin-jun, J.I.N.: Recognition and fault diagnosis of insulator string in aerial images. J. Mech. Electr. Eng. 32(2) (2015)
Yin, J., Lu, Y., Gong, Z., Jiang, Y., Yao, J.: Edge detection of high-voltage porcelain insulators in infrared image using dual parity morphological gradients. IEEE Access 7, 32728–32734 (2019)
Wang, M., Du, Y., Zhang, Z.R.: Study on power transmission lines inspection using unmanned aerial vehicle and image recognition of insulator defect. J. Electron. Meas. Instr. 26, 1862–1869 (2015)
Han, J., et al.: A method of insulator faults detection in aerial images for high-voltage transmission lines inspection. Appl. Sci. 9(10), 2009 (2019)
Siddiqui, Z.A., Park, U.: A drone based transmission line components inspection system with deep learning technique. Energies 13(13), 3348 (2020)
Kang, G., Gao, S., Yu, L., Zhang, D.: Deep architecture for high-speed railway insulator surface defect detection: denoising autoencoder with multitask learning. IEEE Trans. Instr. Meas 68(8), 2679–2690 (2018)
Ma, L., Xu, C., Zuo, G., Bo, B., Tao, F.: Detection method of insulator based on faster R-CNN. In: Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 1410–1414 (2017)
Tao, X., Zhang, D., Wang, Z., Liu, X.: Detection of power line insulator defects using aerial images analyzed with convolutional neural networks. IEEE Trans. Syst. Man Cybern. Syst. 1–13 (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
J., Redmon, S., Divvala, R., Girshick: You only look once: unified, real-time object detection. In: CVPR, pp. 779–788 (2016)
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. Preprint arXiv:2004.10934 (2020)
Nguyen Dinh, C., Delalandre, M., Conte, D., Pham, T.A.: Fast RT-LoG operator for scene text detection. J. Real-Time Image Process. 18(1), 19–36 (2020). https://doi.org/10.1007/s11554-020-00942-7
Acknowledgments
This research was funded by ThinkLABs R&DFootnote 3 and the sample data was supported by EVN NPT2Footnote 4 in Da Nang.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Nguyen, D.C., Nguyen, T.C., Phan, D.H., Le, N.T., Tran, V.V. (2021). Multi-object Detection by Using CNN for Power Transmission Line Inspection. In: Vo, NS., Hoang, VP., Vien, QT. (eds) Industrial Networks and Intelligent Systems. INISCOM 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 379. Springer, Cham. https://doi.org/10.1007/978-3-030-77424-0_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-77424-0_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77423-3
Online ISBN: 978-3-030-77424-0
eBook Packages: Computer ScienceComputer Science (R0)