REFERENCES

1. Deng, D.; Fang, Y.; Zhou, B.; et al. Risk and prevention control analysis of live line operation of distribution network bypass. J. Phys. Conf. Ser. 2024, 2795, 012008.

2. Zhang, W. Research on safety detection technology of power live Working based on deep learning. 2021.

3. Ye, L. Live working risk assessment analysis and countermeasure. 2017. https://kns.cnki.net/kcms2/article/abstract?v=i12nDYUbXpK_qFT2vWx-FtybcbqytCk7CAtXBz7R639GSYnC9KX1PGgipQFbCB21RSLeBTbnKxDeDNjk09rUX8IPyxJf49C0H-KqTxLmwO4RQQvGNcHxNiiTdXEaxKccn1aagToP8kd6gVnrWAOTiwErk-OeFbBNzULHYWwPcyXbyy2qANi4XHafx8bHKY8WzKe4cwRD-xBnAMLBnPkTJA==&uniplatform=NZKPT&language=CHS. (accessed 14 Jan 2025).

4. Guo, J.; Li, X. Very low-resolution object detection algorithms for electric intelligent safety supervision. Comput. Eng. Design. 2020, 41, 9188-92.

5. Peng, G.; Lei, Y.; Li, H.; Wu, D.; Wang, J.; Liu, F. CORY-Net: contrastive Res-YOLOv5 network for intelligent safety monitoring on power grid construction sites. IEEE. Access. 2021, 9, 160461-70.

6. Ma, F.; Wang, B.; Dong, X.; Yao, L.; Wang, H. Safety image interpretation of power industry: basic concepts and technical framework. Proc. CSEE. 2020, 42, 458-75.

7. Ma, F.; Wang, B.; Dong, X.; Luo, P.; Wang, H.; Zhou, Y. Receptive field vision edge intelligent recognition for ice thickness identification of transmission line. Power. Syst. Technol. 2021, 45, 2161-9.

8. Xu, L. Y.; Zhao, Y. F.; Zhai, Y. H.; Huang, L. M.; Ruan, C. W. Small object detection in UAV images based on YOLOv8n. Int. J. Comput. Intell. Syst. 2024, 17, 223.

9. Sindjoung, M. L. F.; Velempini, M.; Djamegni, C. T. A data security and privacy scheme for user quality of experience in a mobile edge computing-based network. Array 2023, 19, 100304.

10. Ma, M. Y.; Shen, S. E.; Huang, Y. C. Enhancing UAV visual landing recognition with YOLO's object detection by onboard edge computing. Sensors 2023, 23, 8999.

11. Luo, P.; Wang, B.; Wang, H.; Ma, F.; Ma, H.; Wang, L. An ultrasmall bolt defect detection method for transmission line inspection. IEEE. Trans. Instrum. Meas. 2023, 72, 1-12.

12. Li, Y.; Gao, D. W.; Gao, W.; Zhang, H.; Zhou, J. Double-Mode energy management for multi-energy system via distributed dynamic event-triggered newton-raphson algorithm. IEEE. Trans. Smart. Grid. 2020, 11, 5339-56.

13. Liu, L. N.; Yang, G. H.; Wasly, S. Distributed predefined-time dual-mode energy management for a microgrid over event-triggered communication. IEEE. Trans. Ind. Informat. 2024, 20, 3295-305.

14. Li, K.; Qin, L.; Li, Q.; Zhao, F.; Xu, Z.; Liu, K. Improved edge lightweight YOLOv4 and its application in on-site power system work. Glob. Energy. Intercon. 2022, 5, 168-80.

15. Girshick, R. Fast R-CNN. arXiv2015, arXiv: 1504.08083. Available online: https://doi.org/10.48550/arXiv.1504.08083 (accessed 14 Jan 2025).

16. Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE. Trans. Pattern. Anal. Mach. Intell. 2017, 39, 1137-49.

17. 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), Las Vegas, USA, June 27-30, 2016. IEEE, 2016; pp. 779-88.

18. Redmon, J.; Farhadi, A. YOLO9000: better, faster, stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, July 21-26, 2017. IEEE, 2017; pp. 7263–71.

19. Redmon, J.; Farhadi, A. YOLOv3: an incremental improvement. arXiv2018, arXiv: 1804.02767. Available online: https://doi.org/10.48550/arXiv.1804.02767(accessed 14 Jan 2025).

20. Bai, Q.; Gao, R.; Li, Q.; Wang, R.; Zhang, H. Recognition of the behaviors of dairy cows by an improved YOLO. Intell. Robot. 2024, 4, 1-19.

21. Zhuang, T.; Liang, X.; Xue, B.; Tang, X. An In-Vehicle real-time infrared object detection system based on deep learning with resource-constrained hardware. Intell. Robot. 2024, 4, 276-92.

22. Liu, W.; Anguelov, D.; Erhan, D.; et al. SSD: single shot multibox detector. In Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, Springer, Cham, 2017; pp. 21-37.

23. Kong, Y.; Wang, W.; Zhang, K.; Qi, Y. Object detection method of electric power site based on improved Mask R-CNN. Sci. Technol. Eng.2020, 20, 3134–42. https://kns.cnki.net/kcms2/article/abstract?v=XRdBcB-NO4SX1qe6ikZV25DYo6aOIz8NCQl_QG8Y5aXyV7FLn5heHXsTgzxytFuheInZE0MyXxtj1QEu20RVZmzfh9jpO4Ukmo-7n2TcCdu234pFct3hhdCbyF3uFrZM-QAY_nJEGdHs-jintp3aGIBnquSkxHsOgo1zl4fIzcVfc3n051-D_FLY7AASc9VfjT-AZTs8oss=&uniplatform=NZKPT&language=CHS. (accessed 14 Jan 2025).

24. Lei, X.; Sui, Z. Intelligent fault detection of high voltage line based on the Faster R-CNN. Measurement 2019, 138, 379-85.

25. Yang, Q.; Ma, S.; Guo, D.; Wang, P.; Lin, M.; Hu, Y. A small object detection method for oil leakage defects in substations based on improved Faster-RCNN. Sensors 2023, 23, 7390.

26. Han, H.; Xue, X.; Li, Q.; et al. Pig-ear detection from the thermal infrared image based on improved YOLOv8n. Intell. Robot. 2024, 4, 20-38.

27. Yuan, L.; Tang, H.; Chen, Y.; Gao, R.; Wu, W. Improved YOLOv5 for road target detection in complex environments. Comput. Eng. Appl.2023, 59, 212-22. https://link.cnki.net/urlid/11.2127.TP.20230607.1151.004(accessed 2024-12-19).

28. Chang, Z.; Peng, Q.; Chen, Y. Safety supervision method for power operation site based on machine learning and image recognition. Electric. Power.2020; 53(04): 155-160. https://link.cnki.net/urlid/11.3265.TM.20200403.0900.006(accessed 14 Jan 2025).

29. Li, H.; Dong, Y.; Liu, Y.; Ai, J. Design and implementation of UAVs for bird's nest inspection on transmission lines based on deep learning. Drones 2022, 6, 252.

30. Zhao, W.; Jia, M.; Zhai, Y.; Zhao, Z. Detection method for Pin-Losing bolts in transmission lines based on improved YOLOv5s. J. North. China. Electric. Power. Univ.202451, 92-100. https://link.cnki.net/urlid/13.1212.TM.20220920.0949.002(accessed 14 Jan 2025).

31. He, M.; Qin, L.; Zhao, F.; et al. Intelligent detection algorithm of security risk management and control for power system on-site operation. High. Volt. Eng. 2023, 49, 2442-57.

32. Panigrahy, S.; Karmakar, S. Real-time condition monitoring of transmission line insulators using the YOLO object detection model with a UAV. IEEE. Trans. Instrum. Meas. 2024, 73, 1-9.

33. Wang, T.; Liu, R.; Zhang, T.; Wu, Y.; Kong, W. Safety detection of live work in distribution networks based on object detection and tracking. Electron Eng Prod World2024, 31, 51–3+68. https://kns.cnki.net/kcms2/article/abstract?v=XRdBcB-NO4SqfKuRSLUWbplAffDbnT3gaSWwRAzeLuCT_H2lxQYzObcTkFM2rT-St_l1qwXkDK7798ZThHRf8q-hgt77UlDdeJlihJK9JDUbxaADEPmZHfDtrJVlHvrfPaJRGGGAL40CoqXog6x76tU9Eq5azvpd-sJ5qp_AfablYHB3bvqDzszdeodJchTK5m6fQEyDCks=&uniplatform=NZKPT&language=CHS (accessed 14 Jan 2025).

34. Li, H. Risk identification technology for power operation site based on deep learning. Yunnan. Electric. Power.2024, 52, 46–9. https://kns.cnki.net/kcms2/article/abstract?v=XRdBcB-NO4TtYcdD_A6AfmNzlwJMK3vfEtKcqEOGIQPkQVqenMt1zENl0wr46EnnW1V5j822fAbCwn4upf1Y_hxAlNaOIOvRE4yFaotsyhGZRzToIwvUNaHS-FOzztv_B8r1R1phBQEbM_uPSRaHL7F07Y1Q8Z5HgHdCfu-vP_9Fzp4ukXlQJwygF_wRotgZMsKSpJNv38Q=&uniplatform=NZKPT&language=CHS (accessed 14 Jan 2025).

35. Lin, T. Y.; Dollár, P.; Girshick, R.; He, K.; Hariharan, B.; Belongie, S. Feature pyramid networks for object detection. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honululu, USA, July 21-26, 2017; IEEE, 2017; pp. 2117-25.

36. Liu, S.; Qi, L.; Qin, H.; Shi, J.; Jia, J. Path aggregation network for instance segmentation. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Sale Lake City, USA, June 18-23, 2018; IEEE, 2018; pp. 8759-68.

37. Zhu, C.; He, Y.; Savvides, M. Feature selective anchor-free module for single-shot object detection. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, June 15-20, 2019; IEEE, 2019; pp. 840-9.

38. Mitiche, I.; Morison, G.; Nesbitt, A.; Hughes-Narborough, M.; Stewart, B. G.; Boreham, P. Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features. Sensors 2018, 18, 406.

39. Zhang, X.; Zhou, X.; Lin, M.; Sun, J. ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, June 18-23, 2018; IEEE, 2018; pp. 6848-56.

40. Chen, J.; Kao, S. H.; He, H.; et al. Run, don't walk: chasing higher FLOPS for faster neural networks. arXiv2023, arXiv: 2303.03667. Available online: https://doi.org/10.48550/arXiv.2303.03667 (accessed 14 Jan 2025).

41. Wang, Q.; Wu, B.; Zhu, P.; Li, P.; Zuo, W.; Hu, Q. ECA-net: efficient channel attention for deep convolutional neural networks. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, June 13-19, 2020; IEEE, 2020; pp. 11534-42.

42. Hu, J.; Shen, L.; Sun, G. Squeeze-and-excitation networks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, June 18-23, 2018; IEEE, 2018; pp. 7132-41.

43. Gao, D.; Chen, T.; Miao, L. Improved road object detection algorithm for YOLOv8n. Comput. Eng. Appl.202460, 186-97. Available online: https://link.cnki.net/urlid/11.2127.TP.20240528.1330.002 (accessed 14 Jan 2025).

44. Ma, S.; Xu, Y. MPDIoU: a loss for efficient and accurate bounding box regression. arXiv2023, arXiv: 2307.07662. Available online: https://doi.org/10.48550/arXiv.2307.07662(accessed 14 Jan 2025).