计算机与数字工程2023，Vol.51Issue(12)：2959-2965,7.DOI:10.3969/j.issn.1672-9722.2023.12.035
深度迁移学习与注意力机制的垃圾图像分类方法
Garbage Image Classification Based on Deep Transfer Learning and Attention Module
摘要
Abstract
Garbage sorting is of great significance to save resources and improve the environment.In response to the growth of garbage types brought by the increasing consumption power,a garbage classification image recognition method based on deep learn-ing neural network and migration learning and introducing attention mechanism is proposed.First,the four major classifications of recyclable waste,food waste,hazardous waste and other waste with national standards are collected and established,including the secondary classification data set with 210 seed classifications.Second,a self-training plus migration learning fusion approach is used to first build a self-training convolutional neural network,followed by VGG16,ResNet50 and ResNeSt50 convolutional neural networks,migrating homogeneous models under the pre-trained feature models are fused with the features extracted from the two networks,and then an improved model based on the CBAM attention mechanism is added,and finally the fine-tuned network is ac-cessed for retraining.The analysis yields the best garbage classification model.The experimental data shows that the method in this paper saves an average of 29.4％ in time consumption and improves the model accuracy by 8.06％ on average compared to the non-migratory learning network,with a maximum accuracy of 92.2％.The approach can significantly improve the efficiency of gar-bage classification automation.关键词
深度学习/垃圾分类/卷积注意力机制模块/迁移学习/微调网络Key words
deep learning/garbage classification/convolutional block attention module(CBAM)/transfer learning/fine-tuning networks分类
信息技术与安全科学引用本文复制引用
王策仁,彭亚雄,陆安江..深度迁移学习与注意力机制的垃圾图像分类方法[J].计算机与数字工程,2023,51(12):2959-2965,7.基金项目
贵州省科技成果转化项目(编号:[2017]4856)资助. （编号:[2017]4856）
