Insulator defect detection based on BaS-YOLOv5
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00530-024-01413-w.pdf
Reference26 articles.
1. Li, Y., Ni, M., Lu, Y.: Insulator defect detection for power grid based on light correction enhancement and YOLOv5 model. Energy Rep. 8, 807–814 (2022). https://doi.org/10.1016/j.egyr.2022.08.027
2. Li, J., Yang, Y.: HM-YOLOv5: A fast and accurate network for defect detection of hot-pressed light guide plates. Eng. Appl. Artif. Intell. 117, 105529 (2023). https://doi.org/10.1016/j.engappai.2022.105529
3. Mahendrakar, T., Ekblad, A., Fischer, N., White, R., Wilde, M., Kish, B., Silver, I.: Performance study of YOLOv5 and faster R-CNN for autonomous navigation around non-cooperative targets. 2022 IEEE Aerosp. Conf. 1–12 (2022). https://doi.org/10.1109/AERO53065.2022.9843537
4. Tang, J., Liu, S., Zhao, D., Tang, L., Zou, W., Zheng, B.: PCB-YOLO: An improved detection algorithm of PCB surface defects based on YOLOv5. Sustainability. 15, 5963 (2023). https://doi.org/10.3390/su15075963
5. Jia, Z., Su, X., Ma, G., Dai, T., Sun, J.: Crack identification for marine engineering equipment based on improved SSD and YOLOv5. Ocean. Eng. 268, 113534 (2023). https://doi.org/10.1016/j.oceaneng.2022.113534
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