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

Authors: Jinxiang Chen; Yiqun Cheng; Jianxin Zhang

Addresses: Inner Mongolia Key Laboratory of Mechanical and Electrical Control, College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China ' Inner Mongolia Key Laboratory of Mechanical and Electrical Control, College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China ' Inner Mongolia Key Laboratory of Mechanical and Electrical Control, College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China

Abstract: 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.

Keywords: hazardous waste classification; occluded target identification; vision transformer model; VTM; MobileNet.

DOI: 10.1504/IJMIC.2023.131203

International Journal of Modelling, Identification and Control, 2023 Vol.42 No.4, pp.333 - 340

Received: 21 May 2022
Received in revised form: 13 Jun 2022
Accepted: 27 Jun 2022
Published online: 01 Jun 2023 *

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