Federated knowledge distillation for enhanced insulator defect detection in resource‐constrained environments
Abstract Insulator defect detection is crucial for the stable operation of power systems. It has become a mainstream research direction to realise insulator defect detection based on the combination of line images captured by UAVs and deep learning techniques. However, the existing high‐quality insu...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Computer Vision |
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| Online Access: | https://doi.org/10.1049/cvi2.12290 |
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| author | Xiaohu Huang Minghui Jia Xianghua Tai Wei Wang Qi Hu Dongping Liu Peiheng Guo Shengxiang Tian Dequan Yan Haishan Han |
| author_facet | Xiaohu Huang Minghui Jia Xianghua Tai Wei Wang Qi Hu Dongping Liu Peiheng Guo Shengxiang Tian Dequan Yan Haishan Han |
| author_sort | Xiaohu Huang |
| collection | DOAJ |
| description | Abstract Insulator defect detection is crucial for the stable operation of power systems. It has become a mainstream research direction to realise insulator defect detection based on the combination of line images captured by UAVs and deep learning techniques. However, the existing high‐quality insulator defect detection models still face problems such as relying on massive‐labelled data and huge model parameters. Especially on resource‐constrained devices, it becomes a challenge to strike a balance between model lightweighting and performance. Although the knowledge distillation technique provides a solution for model lightweighting, the loss of information in the distillation process leads to the performance degradation of small models, which in turn creates a paradox between lightweighting and performance. Hence, an insulator defect detection method based on federated knowledge distillation is proposed. The method not only realises the lightweighting of the model, but also effectively improves the model performance by collaboratively training the model through the federated learning approach. Moreover, the asynchronous aggregation approach and model freshness mechanism designed in the method further enhance the training efficiency and collaborative effect. The experimental results show that the detection accuracy and efficiency of this paper's method on public datasets are significantly better than the benchmark algorithm. |
| format | Article |
| id | doaj-art-b82995fb3ece44ac809d6b71ed0e8c9d |
| institution | DOAJ |
| issn | 1751-9632 1751-9640 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj-art-b82995fb3ece44ac809d6b71ed0e8c9d2025-08-20T02:51:11ZengWileyIET Computer Vision1751-96321751-96402024-12-011881072108610.1049/cvi2.12290Federated knowledge distillation for enhanced insulator defect detection in resource‐constrained environmentsXiaohu Huang0Minghui Jia1Xianghua Tai2Wei Wang3Qi Hu4Dongping Liu5Peiheng Guo6Shengxiang Tian7Dequan Yan8Haishan Han9State Grid Qinghai Electric Power Company Ultra High Voltage Company Xi Ning Qinghai ChinaState Grid Qinghai Electric Power Company Ultra High Voltage Company Xi Ning Qinghai ChinaState Grid Qinghai Electric Power Company Ultra High Voltage Company Xi Ning Qinghai ChinaState Grid Qinghai Electric Power Company Ultra High Voltage Company Xi Ning Qinghai ChinaState Grid Qinghai Electric Power Company Ultra High Voltage Company Xi Ning Qinghai ChinaState Grid Qinghai Electric Power Company Ultra High Voltage Company Xi Ning Qinghai ChinaState Grid Qinghai Electric Power Company Ultra High Voltage Company Xi Ning Qinghai ChinaState Grid Qinghai Electric Power Company Ultra High Voltage Company Xi Ning Qinghai ChinaState Grid Qinghai Electric Power Company Ultra High Voltage Company Xi Ning Qinghai ChinaState Grid Qinghai Electric Power Company Ultra High Voltage Company Xi Ning Qinghai ChinaAbstract Insulator defect detection is crucial for the stable operation of power systems. It has become a mainstream research direction to realise insulator defect detection based on the combination of line images captured by UAVs and deep learning techniques. However, the existing high‐quality insulator defect detection models still face problems such as relying on massive‐labelled data and huge model parameters. Especially on resource‐constrained devices, it becomes a challenge to strike a balance between model lightweighting and performance. Although the knowledge distillation technique provides a solution for model lightweighting, the loss of information in the distillation process leads to the performance degradation of small models, which in turn creates a paradox between lightweighting and performance. Hence, an insulator defect detection method based on federated knowledge distillation is proposed. The method not only realises the lightweighting of the model, but also effectively improves the model performance by collaboratively training the model through the federated learning approach. Moreover, the asynchronous aggregation approach and model freshness mechanism designed in the method further enhance the training efficiency and collaborative effect. The experimental results show that the detection accuracy and efficiency of this paper's method on public datasets are significantly better than the benchmark algorithm.https://doi.org/10.1049/cvi2.12290big datalearning (artificial intelligence)mobile computingobject detection |
| spellingShingle | Xiaohu Huang Minghui Jia Xianghua Tai Wei Wang Qi Hu Dongping Liu Peiheng Guo Shengxiang Tian Dequan Yan Haishan Han Federated knowledge distillation for enhanced insulator defect detection in resource‐constrained environments IET Computer Vision big data learning (artificial intelligence) mobile computing object detection |
| title | Federated knowledge distillation for enhanced insulator defect detection in resource‐constrained environments |
| title_full | Federated knowledge distillation for enhanced insulator defect detection in resource‐constrained environments |
| title_fullStr | Federated knowledge distillation for enhanced insulator defect detection in resource‐constrained environments |
| title_full_unstemmed | Federated knowledge distillation for enhanced insulator defect detection in resource‐constrained environments |
| title_short | Federated knowledge distillation for enhanced insulator defect detection in resource‐constrained environments |
| title_sort | federated knowledge distillation for enhanced insulator defect detection in resource constrained environments |
| topic | big data learning (artificial intelligence) mobile computing object detection |
| url | https://doi.org/10.1049/cvi2.12290 |
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