Insulator Anomaly Detection Method Based on Few-Shot Learning
Due to the advantages of safety and economy, it has become a trend to use unmanned aerial vehicles (UAVs) instead of humans to inspect high-voltage transmission lines. Considering the manual inspection process and the few-shot learning, a two-stage method for insulator anomaly detection is proposed....
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9395571/ |
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doaj-b5f77e07e0c64723b2403d722da574d62021-07-12T23:00:12ZengIEEEIEEE Access2169-35362021-01-019949709498010.1109/ACCESS.2021.30713059395571Insulator Anomaly Detection Method Based on Few-Shot LearningZhaoyang Wang0https://orcid.org/0000-0002-4944-3924Qiang Gao1Dong Li2https://orcid.org/0000-0002-7847-5511Junjie Liu3https://orcid.org/0000-0002-8827-1141Hongwei Wang4Xiao Yu5https://orcid.org/0000-0001-5762-2503Yipin Wang6Tianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems, School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin, ChinaTianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems, School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin, ChinaTianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems, School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin, ChinaTianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems, School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin, ChinaTianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems, School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin, ChinaTianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems, School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin, ChinaState Grid Tianjin Electric Power Company, Tianjin, ChinaDue to the advantages of safety and economy, it has become a trend to use unmanned aerial vehicles (UAVs) instead of humans to inspect high-voltage transmission lines. Considering the manual inspection process and the few-shot learning, a two-stage method for insulator anomaly detection is proposed. In the first stage, a positioning-restoration-cropping method is discussed for insulator string detection and processing. In the second stage, an insulator anomaly detection model called a multi-scale feature reweighting (MFR) network is built. With the help of few-shot object detection, the detection of five kinds of anomaly insulator caps, such as falling off, breakage and ablation is realized. The mean average precision (mAP) of the proposed method is 88.76%.https://ieeexplore.ieee.org/document/9395571/Convolutional neural networkfew-shot learningobject detectioninsulator anomaly detection |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Zhaoyang Wang Qiang Gao Dong Li Junjie Liu Hongwei Wang Xiao Yu Yipin Wang |
| spellingShingle |
Zhaoyang Wang Qiang Gao Dong Li Junjie Liu Hongwei Wang Xiao Yu Yipin Wang Insulator Anomaly Detection Method Based on Few-Shot Learning IEEE Access Convolutional neural network few-shot learning object detection insulator anomaly detection |
| author_facet |
Zhaoyang Wang Qiang Gao Dong Li Junjie Liu Hongwei Wang Xiao Yu Yipin Wang |
| author_sort |
Zhaoyang Wang |
| title |
Insulator Anomaly Detection Method Based on Few-Shot Learning |
| title_short |
Insulator Anomaly Detection Method Based on Few-Shot Learning |
| title_full |
Insulator Anomaly Detection Method Based on Few-Shot Learning |
| title_fullStr |
Insulator Anomaly Detection Method Based on Few-Shot Learning |
| title_full_unstemmed |
Insulator Anomaly Detection Method Based on Few-Shot Learning |
| title_sort |
insulator anomaly detection method based on few-shot learning |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2021-01-01 |
| description |
Due to the advantages of safety and economy, it has become a trend to use unmanned aerial vehicles (UAVs) instead of humans to inspect high-voltage transmission lines. Considering the manual inspection process and the few-shot learning, a two-stage method for insulator anomaly detection is proposed. In the first stage, a positioning-restoration-cropping method is discussed for insulator string detection and processing. In the second stage, an insulator anomaly detection model called a multi-scale feature reweighting (MFR) network is built. With the help of few-shot object detection, the detection of five kinds of anomaly insulator caps, such as falling off, breakage and ablation is realized. The mean average precision (mAP) of the proposed method is 88.76%. |
| topic |
Convolutional neural network few-shot learning object detection insulator anomaly detection |
| url |
https://ieeexplore.ieee.org/document/9395571/ |
| work_keys_str_mv |
AT zhaoyangwang insulatoranomalydetectionmethodbasedonfewshotlearning AT qianggao insulatoranomalydetectionmethodbasedonfewshotlearning AT dongli insulatoranomalydetectionmethodbasedonfewshotlearning AT junjieliu insulatoranomalydetectionmethodbasedonfewshotlearning AT hongweiwang insulatoranomalydetectionmethodbasedonfewshotlearning AT xiaoyu insulatoranomalydetectionmethodbasedonfewshotlearning AT yipinwang insulatoranomalydetectionmethodbasedonfewshotlearning |
| _version_ |
1721306870786818048 |