Image classification of insect pests based on saliency detection

被引：0
|
作者：
Zhao H.-W. [1 ]
Huo D.-S. [1 ]
Wang J. [2 ]
Li X.-N. [3 ]
机构：
[1] College of Computer Science and Technology, Jilin University, Changchun
[2] Shanghai Academy of Spaceflight Technology, Shanghai
[3] College of Computer Science and Technology, Changchun Normal University, Changchun
来源：
Li, Xiao-Ning (lixiaoning@ccsfu.edu.cn) | 1600年 / Editorial Board of Jilin University卷 / 51期
关键词：
Computer application; Data enhancement; insect pest classification; Multi-feature fusion; Significance detection;
D O I：
10.13229/j.cnki.jdxbgxb20200749
中图分类号：
学科分类号：
摘要：
Due to the diverse species of pests and the large differences between classes, it is difficult to achieve accurate classification. Based on the two processes of target positioning and target recognition when the human visual system recognizes objects, a pest classification model PestNet is designed. The model is mainly composed of Object Positioning Module (OPM) and Multi-Feature Fusion Module (MFFM). OPM integrates the shallow detail information and deep spatial information of the image through a U-shaped network structure, preliminarily delineating significant areas, and outputting spatial semantic features. MFFM performs bilinear pooling operations on spatial semantic features and abstracts semantic features to weaken background information and increase detailed features. In addition, training is assisted by target area clipping and masking to improve model classification accuracy. We conducted experiments on the disease and insect pest data set IP102, and the model classification accuracy rate reached 77.40%, which can realize the classification and recognition of large-scale pest images under complex background. © 2021, Jilin University Press. All right reserved.
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页码：2174 / 2181
页数：7
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