Abstract
The big growth of electrical demand by the countries required larger and more complex power systems, which have led to a greater need for monitoring and maintenance of these systems. To overcome this problem, UAVs equipped with appropriated sensors have emerged, allowing the reduction of the costs and risks when compared with traditional methods. The development of UAVs together with the great advance of the deep learning technologies, more precisely in the detection of objects, allowed to increase the level of automation in the process of inspection. This work presents an electrical assets monitoring system for detection of insulators and structures (poles and pylons) from images captured through a UAV. The proposed detection system is based on lightweight Convolutional Neural Networks and it is able to run on a portable device, aiming for a low cost, accurate and modular system, capable of running in real time.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Malveiro, M., Martins, R., Carvalho, R.: Inspection of high voltage overhead power lines with UAV’s. In: Proceedings of the 23rd International Conference on Electricity Distribution (2015)
Luque-Vega, L.F., Castillo-Toledo, B., Loukianov, A., Gonzalez-Jimenez, L.E.: Power line inspection via an unmanned aerial system based on the quadrotor helicopter. In: MELECON 2014 - 2014 17th IEEE Mediterranean Electrotechnical Conference (2014)
Deng, C., Wang, S., Huang, Z., Tan, Z., Liu, J.: Unmanned aerial vehicles for power line inspection: a cooperative way in platforms and communications. J. Commun. 9(9), 687–692 (2014)
Menendez, O.A., Perez, M., Cheein, F.A.A.: Vision based inspection of transmission lines using unmanned aerial vehicles. In: 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) (2016)
Xie, X., Liu, Z., Xu, C., Zhang, Y.: A multiple sensors platform method for power line inspection based on a large unmanned helicopter. Sensors 17(6), 1222 (2017)
Jabid, T., Ahsan, T.: Insulator detection and defect classification using rotation invariant local directional pattern. Int. J. Adv. Comput. Sci. Appl. 9(2), 265–272 (2018)
Siddiqui, Z., Park, U., Lee, S.W., Jung, N.J., Choi, M., Lim, C., Seo, J.H.: Robust powerline equipment inspection system based on a convolutional neural network. Sensors 18(11), 3837 (2018)
Tao, X., Zhang, D., Wang, Z., Liu, X., Zhang, H., Xu, D.: Detection of power line insulator defects using aerial images analyzed with convolutional neural networks. IEEE Trans. Syst. Man Cybern.: Syst. PP(99), 1–13 (2018)
Hui, X., Bian, J., Zhao, X., Tan, M.: Vision-based autonomous navigation approach for unmanned aerial vehicle transmission-line inspection. Int. J. Adv. Robot. Syst. 15(1), 1729881417752821 (2018)
Hui, X., Bian, J., Zhao, X., Tan, M.: Deep-learning-based autonomous navigation approach for UAV transmission line inspection. In: 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI) (2018)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C., Berg, A.C.: SSD: single shot multibox detector. CoRR (2015)
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: efficient convolutional neural networks for mobile vision applications. CoRR (2017)
Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.: Inverted residuals and linear bottlenecks: mobile networks for classification, detection and segmentation. CoRR (2018)
Wang, R.J., Li, X., Ao, S., Ling, C.X.: Pelee: a real-time object detection system on mobile devices. CoRR (2018)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. CoRR (2018)
Redmon, J.: Darknet: Open source neural networks in c (2013–2016). http://pjreddie.com/darknet/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Barbosa, J., Dias, A., Almeida, J., Silva, E. (2020). Evaluation of Lightweight Convolutional Neural Networks for Real-Time Electrical Assets Detection. In: Silva, M., Luís Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1092. Springer, Cham. https://doi.org/10.1007/978-3-030-35990-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-35990-4_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35989-8
Online ISBN: 978-3-030-35990-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)