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Cross-class pest and disease vegetation detection based on small sample registration

文献类型： 外文期刊

作者： Liu, Jiayao 1 ; Wang, Linfeng 1 ; Wang, Yunsheng 2 ; An, MingMing 2 ; Jiang, Wenfei 2 ; Xu, Shipu 2 ;

作者机构： 1.Shanghai Inst Technol, Sch Railway Transportat, Shanghai, Peoples R China

2.Shanghai Acad Agr Sci, Inst Agr Informat Sci & Technol, Shanghai, Peoples R China

3.Shanghai Acad Agr Sci, Inst Agr Informat Sci & Technol, Shanghai 201403, Peoples R China

关键词： cross-training set; few-shot learning; pest detection

期刊名称：IET IMAGE PROCESSING （ 影响因子：2.3； 五年影响因子：2.3 ）

ISSN： 1751-9659

年卷期： 2023 年 17 卷 8 期

页码：

收录情况： SCI

摘要： This paper introduces few-shot anomaly detection (FSAD), a practical and less anomaly detection (AD) method, which can provide a limited number of normal images for each class during training. So far, studies on FSAD have been carried out according to each model, and there is no discussion of commonalities between different types. Depending on how people detect unusual lies, the problematic images are compared to the normal ones. The image alignment method based on different classifications is used to train the target detection model independent of classification, and performed ablation experiments on the pest and disease datasets in different environments for verification. This is the first time the FSAD method has been used to train a single scalable model without the need to train new classifications or adjust parameters. The experimental results show that the application of AUC based on vegetation disease data set and vegetation pest data set in FSAD algorithm is improved by 19.5% compared with the existing algorithm.

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