基于显著性检测的害虫图像分类

doi:10.13229/j.cnki.jdxbgxb20200749

Abstract

Abstract:

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.

Key words: computer application, insect pest classification, significance detection, multi-feature fusion, data enhancement

CLC Number:

TP391

Hong-wei ZHAO,Dong-sheng HUO,Jie WANG,Xiao-ning LI. Image classification of insect pests based on saliency detection[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2174-2181.

Figures/Tables 8

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基础模型	图像尺寸	准确率/%
VGG-16_［Mask］	224×256	67.90
Inception-V3_［Mask］	299×331	70.88
ResNet-50_［Mask］	224×256	71.57
ResNet-50_［Mask］	448×512	73.27
ResNet-50_［OPM］	448×512	74.34

OPM	MFFM	裁剪	掩膜	准确率/%
	√			73.33
√				74.34
√	√			75.61
√	√	√		76.74
√	√		√	76.03
√	√	√	√	77.40

Image classification of insect pests based on saliency detection

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