Protection Plate Status Recognition Based on Few-Shot Learning and Knowledge Transfer
Aiming at the issue of automatic recognition of the switch status of the protection plate in the substation,a recognition model of the protection plate switch state based on few-shot learning and knowledge transfer is proposed.The residual network is used to ex-tract image features,and the measurement method is used to calculate the difference between the query image and the support image.Based on the similarity,KNN is used to realize the classification and recognition of the switch state of the protection plate.The residual network pre-trained model on the public data set is direct transferred to the recognition task of the protection plate switch state based on few-shot learning,and the influence of different number of nearest neighbors in KNN algorithm on the classification results of the switch state of the protection plate is studied.The proposed method can realize the recognition of the switch state of the protection plate when there are few image samples.The experimental results show that when the number of supporting samples is 30,the image recognition ac-curacy reaches 99.49%.Compared with other large sample classification methods,the proposed classification method using a small num-ber of samples can achieve satisfactory classification results and improve the efficiency of image classification.
few-shot learningtransfer learningprotection plate state recognition
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