Lightweight network for insulator fault detection based on improved YOLOv5
Severe damage to insulators can hinder the daily operation of the power system. Current fault diagnosis methods heavily depend on manual visual inspection, leading to inefficiency and inaccuracies. While computer vision algorithms have been developed, their high requirements for running environments...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/09540091.2023.2284090 |
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