Abstract
Ultra High Voltage (UHV) transmission is the most advanced transmission technology in the world. However, it is difficult for the daily maintenance of high voltage power towers. Based on the development of robots and in-depth learning, this paper proposes a visual-based pylon climbing robot to detect high-voltage tower nuts. An improved yolov5 is developed by adding coordinate attention (CA) module to the backbone, and assigning different weights to different levels of features, replacing the Concat of neck species with Full-Concat. Experiment results showed that our proposed scheme can detect and locate nuts very well, and our trained model can also be well applied in our devices.
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
Zixing, C.: Fundamentals of Robotics. China Machine Press, Beijing (2009)
Haihua, F.U.: Identification of handwriting by writing robot. Sci. Technol. Winds 19, 4 (2019)
Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: Unified, realtime object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788 (2016)
Yang, X., Jiang, W., Yuan, H.: traffic sign recognition and detection based on Yolov5. Inf. Technol. Inform. (4), 28–30 (2021)
Ouyang, W., Zeng, X., Wang, X., et al.: DeepID-net: deformable deep convolutional neural networks for object detection. IEEE Trans. Patt. Anal. Mach. Intell. 39, 1 (2016)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708 (2017)
Lin, T.Y., Dollar, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 936–944 (2017)
Liu, S., Qi, L., Qin, H., et al.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768 (2018)
Hou, Q.B., Zhou, D.Q., Feng, J.S.: Coordinate attention for efficient mobile network design. In: Proceeding of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713–13722 (2021)
Haibo, L., Shanli, T., Shuang, S., Haoran, L.: An improved yolov3 algorithm for pulmonary nodule detection. In: 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), vol. 4, pp. 1068–1072 (2021)
Acknowledgement
This work was supported by the State Grid Anhui Electric Power Co., Ltd. (No. 5212002000AS).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Cheng, Z., Luo, Y., Zhang, J., Gong, Z., Sun, L., Xu, L. (2022). High-Voltage Tower Nut Detection and Positioning System Based on Binocular Vision. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13393. Springer, Cham. https://doi.org/10.1007/978-3-031-13870-6_37
Download citation
DOI: https://doi.org/10.1007/978-3-031-13870-6_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13869-0
Online ISBN: 978-3-031-13870-6
eBook Packages: Computer ScienceComputer Science (R0)