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Insulator Anomaly Detection Method Based on Few-Shot Learning
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DOI：10.1109/ACCESS.2021.3071305.png)
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摘要
En 中文
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%.
Keyword:
Insulators
Feature extraction
Object detection
Inspection
Training
Anomaly detection
Discharges (electric)
Convolutional neural network
few-shot learning
object detection
insulator anomaly detection
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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