Abstract
With the progress of technology and the improvement of equipment quality, the coverage of China’s transmission network is expanding rapidly. Large power grids across complex and volatile terrain and dangerous high-voltage transmission lines are also being extended. Therefore, the traditional method of checking the circuit manually is no longer feasible due to its low efficiency, low precision, high risk and high cost. However, unmanned aerial vehicles (uavs) are a perfect way to circumvent these problems by inspecting transmission lines instead of workers. This paper takes the application of unmanned aerial vehicle in power line patrol as the research background, takes the porcelain vase in power transmission as an example, and realizes the image recognition and damage judgment system of the porcelain vase with specific target. Based on the image processing technology of machine learning and MATLAB, the target detection method of YOLO v3, the semantic segmentation method of Deeplab v3+, and the improved damage analysis method of ellipse fitting were respectively used to make the damage judgment and analysis of porcelain vats based on the intelligent image recognition interception, contour extraction and semantic segmentation. In the actual site of 166 porcelain bottles damage detection, damage detection accuracy reached 86.7%. Finally, the identification system of porcelain vase defect is realized.
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References
Peng, X., Liu, Z., Mai, X., Luo, Z., Wang, K., Xie, X.: UAV power line safety inspection system and key technologies. Remote Sens. Inf. 51–57 (2015)
Yao, W.: Research on UAV Power Line Inspection Technology, Guangdong University of Technology (2019)
Alvarez, L.M.: A visual servoing approach for tracking features in urban areas using an autonomous helicopter. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006, pp. 2503–2508 (2006)
Chen, Y.: UAV Power Line Detection Based on Image Recognition. Hangzhou Dianzi University (2018)
Chang, C.: Special issue on development of autonomous unmanned aerial vehicles. Mechatronics 21(5) (2011)
Fu,Y., Li, Z., Jiang, H.: Research on the development and application of UAV line inspection. Heilongjiang Sci. Technol. Inform. (2014)
Lu, J.: Application of image processing technology in UAV power line inspection. Commun. Power Technol. 36(06), 84–85 (2019)
Luo, X.: Research on UAV Power Inspection Route Planning Based on Fish School Algorithm . Nanchang University (2019)
Miao, X., Liu, Z., Yan, Q.: Overview of UAV transmission line intelligent inspection technology. J. Fuzhou Univ. (Natl. Sci. Ed.) 48(02), 198–209 (2020)
Redmon, J., Divvala, S., Girshick, R., 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 (2015)
Redmon, J., Ali, F.: YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517–6525 (2017)
Lv, H.: Design and implementation of automatic aiming system based on YOLO. In: Sun Yat-sen University, East China Normal University, Singapore International Association for Computer Science and Information Technology, pp. 426–432 (2019)
Guo, J., Chen, B., Wang, R., Wang, J., Zhong, L.: Real-time inspection of UAV power line tower inspection images based on YOLO. China Electr. Power 52(07), 17–23 (2019)
Ruan, J.: Design and Implementation of Target Detection Algorithm Based on YOLO. Beijing University of Posts and Telecommunications (2019)
Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement. ArXiv (2018)
Liu, L.: Research on Intelligent Traffic Traffic Statistics Based on YOLO Network. Xi’an University of Science and Technology (2019)
Fang, Z.: Research on pedestrian detection technology in road traffic environment based on YOLOv3 . South China University of Technology (2019)
Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.: Semantic image segmentation with deep convolutional nets and fully connected CRFs (2014)
Wang, Y., Feng, F.: Road scene semantic segmentation method based on fully connected conditional random field. Comput. Knowl. Technol. 15(18), 212–214 (2019)
Zhang, Q., Zhao, X.: Application of SIFT algorithm in feature extraction of UAV remote sensing images. Henan Water Conserv. South-to-North Water Diversion 48(11), 63–65 (2019)
Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Analy. Mach. Intell. 40(4), 834–848 (2018)
Chen, L., Papandreou, G., Schroff, F., Adam, H.: Rethinking Atrous Convolution for Semantic Image Segmentation (2017)
Teichmann, M.T., Cipolla, R.: Convolutional CRFs for Semantic Segmentation (2018)
Ren, F., He, X., Wei, Z., Lu, Y., Li, M.: Semantic segmentation based on DeepLabV3+ and superpixel optimization. Opt. Precis. Eng. 27(12), 2722–2729 (2019)
Chen, L, Yukun, Z., George, P., Florian, S., Hartwig, A.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818 (2018)
Zhao, Y., Rao, Y., Dong, S., Zhang, J.: A review of deep learning target detection methods. J. Image Graph. 25(04), 629–654 (2020)
Varghese, A., Gubbi, J., Sharma, H., Balamuralidhar, P.: Power infrastructure monitoring and damage detection using drone captured images. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1681–1687 IEEE (2017)
Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.: DeepLab: semantic image segmentation with deep convolutional nets, atrous, convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)
Kuang, H., Wu, J.: A review of research on image semantic segmentation technology based on deep learning. Comput. Eng. Appl. 55(19), 12–21 (2019)
Yang, W.: Research on key technologies and methods of image semantic segmentation based on deep learning . Nanjing University of Posts and Telecommunications (2019)
Liu, Z., Zhang, Z.: Overview of semantic object segmentation technology. J. Shanghai Univ. (Natl. Sci. Ed.) 477–484 (2007)
Zhao, X., et al:. A review of semantic segmentation algorithms based on deep learning . Shanghai Aerosp. 36(05), 71–82 (2019)
Hu, T., Li, W., Qin, X.: Overview of image semantic segmentation methods. Measur. Control Technol. 38(07), 8–12 (2019)
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Li, Z., Zheng, Z., Shi, S., Rui, E. (2022). Design of Porcelain Insulator Defect Recognition System Based on UAV Line Inspection Image. In: Shi, S., Ma, R., Lu, W. (eds) 6GN for Future Wireless Networks. 6GN 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 439. Springer, Cham. https://doi.org/10.1007/978-3-031-04245-4_35
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