Author:
Gu Yingbin,Huang Peifeng,Wang Juan,Tang Lize,Weng Jia,Wang Xiaofeng
Publisher
Springer Nature Singapore
Reference10 articles.
1. She, L., Fan, Y., Wang, J., et al.: Insulator surface breakage recognition based on multiscale residual neural network. IEEE Trans. Instrument. Measure. 70, 1–9 (2021)
2. Park, K.-C., Motai, Y., Yoon, J.R.: Acoustic fault detection technique for high-power insulators. IEEE Trans. Indust. Electron. 64(12), 9699–9708 (2017). https://doi.org/10.1109/TIE.2017.2716862
3. Qiu, Z., Zhu, X., Liao, C., et al.: Detection of transmission line insulator defects based on an improved lightweight YOLOv4 model. Appl. Sci. 64(12), 12(3), 1207 (2022)
4. Ling, Z.N., Zhang, D.X., Qiu, R.C.: An accurate and re-al-time method of self-blast glass insulator location based on faster R-CNN and U-net with aerial images. CSEE J. Power Energy Syst. 5(4), 474–482 (2019)
5. Hao, K., Chen, G., Zhao, L., Li, Z., Liu, Y., Wang, C.: An insulator defect detection model in aerial images based on multiscale feature pyramid network. IEEE Trans. Instrument. Measure. 71, 1–12 (2022)