1. Hebei Key Laboratory of Measurement Technology and Instrument, Yanshan University, Qinhuangdao,Hebei 066004, China
2. School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
Abstract In view of the fact that the existing single fine-grained identification model cannot identify the flower subclasses without training samples, a method of mapping fine-grained features to high-dimensional space automatic classification is proposed in combination with destruction and construction learning (DCL) and KNN to realize the classification of the subclasses without training samples. At the same time, in view of the similar characteristics of the same flower subclass and the possible imbalance of samples between classes, the loss function of the DCL model was improved. contrastive loss was used to increase the class spacing of the subclass, and focal loss balanced class Loss. Finally, the experiment was carried out on the peony flower with unbalanced 308 samples. The experimental results showed that the accuracy of the training sample subclass after the improved algorithm was 0.932, and the F1 score was 0.925, which was greatly improved compared with the original DCL algorithm. Meanwhile, the accuracy of the subclass without training sample was 0.903, and the F1 score was 0.888.
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2021, Vol. 42 
