Attention Mechanism Assisted Deep Learning Algorithm for Multi-Category Household Waste Classification

Shoufei Han, Xu Liu, Xiaojing Liu, Honghao Zhu, Ruyi Han

Abstract: With the improvement of people's living standards, the annual global production of waste continues to rise, but the traditional household waste classification methods are burdened with a heavy task due to the wide variety of waste types and the difficulty of identifying them. Deep learning based waste image classification methods can accurately classify the waste images. In this paper, Visual Geometry Group (VGG16) is used to handle multi-category household waste classification. In order to further improve its classification accuracy, an attention mechanism is incorporated into it, and thus a VGG16 deep learning model with attention mechanism (VGG16-AM) is proposed. Experimental results show that the classification accuracy of our proposed model on the waste dataset is significantly improved to 93 % compared to other deep learning algorithms.

External IDs:dblp:conf/icnsc/HanLLZH24