REFERENCES

1. Zuo, F.; Liu, J.; Fu, M.; Lu, J.; Liu, H. An effective detection method for complex weld defects based on adaptive feature pyramid. In 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Yibin, China. Sep 22-24, 2023. IEEE; 2023. p. 1-5.

2. Li, S. B.; Yang, J.; Wang, Z.; Zhu, S. D.; Yang, G. C. Review of development and application of defect detection technology. Acta. Autom. Sin. 2020, 46, 2319-36.

3. Kang, S.; Chen, C.; Zhao, S.; Luo, Y.; Kong, X. Study on infrared image enhancement of wind turbine blades based on adaptive differential multiscale morphology (ADMM). China. Mech. Eng. 2021, 32, 786-92.

4. Li, M.; Wang, H.; Wan, Z. Surface defect detection of steel strips based on improved Yolov4. Comput. Electr. Eng. 2022, 102, 108208.

5. Ahmed, M. D. F.; Mohanta, J. C.; Sanyal, A. Inspection and identifcation of transmission line insulator breakdown based on deep learning using aerial images. Electr. Power. Syst. Res. 2022, 211, 108199.

6. Yang, Z.; Xu, Z.; Wang, Y. Bidirection-fusion-YOLOv3: an improved method for insulator defect detection using uav image. IEEE. Trans. Instrum. Meas. 2022, 71, 1-8.

7. Pan, L.; Chen, L.; Zhu, S.; Tong, W.; Guo, L. Research on small sample data-driven inspection technology of UAV for transmission line insulator defect detection. Information 2022, 13, 276.

8. Dian, S.; Zhong, X.; Zhong, Y. Faster R-transformer: an efficient method for insulator detection in complex aerial environments. Measurement 2022, 199, 111238.

9. Dong, C.; Zhang, K.; Xie, Z.; et al. Transmission line key components and defects detection based on meta-learning. IEEE. Trans. Instrum. Meas. 2024, 73, 1-13.

10. Chang, R.; Xiao, P.; Wan, H.; Li, S.; Zhou, C.; Li, F. A transmission line defect detection method based on YOLOv7 and multi-UAV collaboration platform. J. Electr. Comput. Eng. 2023.

11. Hao, S.; Ren, K.; Li, J.; Ma, X. Transmission line defect target-detection method based on GR-YOLOv8. Sensors 2024, 24, 6838.

12. Wang, Z.; Liu, Z.; Xu, G.; Cheng, S. Object detection in UAV aerial images based on improved YOLOv7-tiny. In 2023 4th International Conference on Computer Vision, Image and Deep Learning (CVIDL), Zhuhai, China. May 12-14, 2023. IEEE; 2023. pp. 370-4.

13. 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.

14. Zhao, Z.; Guo, G.; Zhang, L.; Li, Y. A new anti-vibration hammer rust detection algorithm based on improved YOLOv7. Energy. Rep. 2023, 9, 345-51.

15. Souza, B. J.; Stefenon, S. F.; Singh, G.; Freire, R. Z. Hybrid-YOLO for classification of insulators defects in transmission lines based on UAV. Int. J. Electr. Power. Energy. Syst. 2023, 148, 108982.

16. Dai, Z. Uncertainty-aware accurate insulator fault detection based on an improved YOLOX model. Energy. Rep. 2022, 8, 12809-21.

17. Redmon, J.; Farhadia, A. YOLO9000: better, faster, stronger. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA. Jul 21-26, 2017. IEEE; 2017. pp. 6517-25.

18. Redmon, J.; Farhadi, A. YOLOv3: an incremental improvement. arXiv 2018, arXiv:1804.02767. https://doi.org/10.48550/arXiv.1804.02767. (accessed 19 May 2025).

19. Tian, Y.; Ye, Q.; Doermann, D. YOLOv12: attention-centric real-time object detectors. arXiv 2025, arXiv:2502.12524. https://doi.org/10.48550/arXiv.2502.12524. (accessed 19 May 2025).

20. Liu, S.; Zha, J.; Sun, J.; Li, Z.; Wang, G. EdgeYOLO: an edge-real-time object detector. In 2023 42nd Chinese Control Conference (CCC), Tianjin, China. Jul 24-26, 2023. IEEE; 2023. pp. 7507-12.

21. Wang, C. Y.; Bochkovskiy, A.; Liao, H. Y. M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada. Jun 17-24, 2023. IEEE; 2023. pp. 7464-75.

22. Ding, X.; Zhang, X.; Ma, N.; et al. RepVGG: making VGG-style ConvNets great again. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, USA. Jun 20-25, 2021. IEEE; 2021. pp. 13728-37.

23. Tian, Z.; Shen, C.; Chen, H.; He, T. FCOS: fully convolutional one-stage object detection. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea. Oct 27 - Nov 02, 2019. IEEE; 2019. pp. 9626-35.

24. Liu, R.; Lehman, J.; Molino, P.; et al. An intriguing failing of convolutional neural networks and the CoordConv solution. arXiv 2018, arXiv:1807.03247. https://doi.org/10.48550/arXiv.1807.03247. (accessed 19 May 2025).

25. Zhang, H.; Zhang, S. Shape-IoU: more accurate metric considering bounding box shape and scale. arXiv 2023, arXiv:2312.17663. https://doi.org/10.48550/arXiv.2312.17663. (accessed 19 May 2025).