Anti-local occlusion intelligent classification method based on MobileNet for hazardous waste

被引：1

作者：

Chen, Jinxiang ^{[1
]}

Cheng, Yiqun ^{[1
]}

Zhang, Jianxin ^{[1
]}

机构：

[1] Inner Mongolia Univ Technol, Coll Elect Power, Inner Mongolia Key Lab Mech & Elect Control, Hohhot 010080, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL | 2023年 / 42卷 / 04期

关键词：

hazardous waste classification; occluded target identification; vision transformer model; VTM; MobileNet; SEMANTIC SEGMENTATION;

D O I：

10.1504/IJMIC.2023.131203

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Anti-local occlusion intelligent classification methods based on MobileNet and VTM for hazardous waste are investigated in this paper. Three image datasets with ten kinds of hazardous waste and 5,000.0 samples are constructed, which include the image dataset with without occlusion, the image dataset with 15% occlusion, and the image dataset with random occlusion. Based on them, the MobileNet and VTM intelligent classification model are constructed, trained, and tested, respectively. It can be seen from testing results that the classification accuracies of VTM and MobileNet are very high for the image dataset with without occlusion. But as occlusion area on images go up or randomly changes, the classification accuracies of VTM and MobileNet go down for 15% and random occlusion cases. The testing results show that classification accuracy of MobileNet model is better than that of VTM model for hazardous waste with or without occlusion.

引用

页码：333 / 340

页数：9

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