基于改进EfficientNetV2的有害垃圾图像分类方法

doi:10.11896/jsjkx.211100100

计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211100100-5.doi: 10.11896/jsjkx.211100100

• 图像处理&多媒体技术 • 上一篇下一篇

基于改进EfficientNetV2的有害垃圾图像分类方法

原慧琳¹, 刘军涛², 黄碧², 韩真², 冯宠²

1 东北大学秦皇岛分校管理学院河北秦皇岛 066004
2 东北大学信息科学与工程学院沈阳 110819

出版日期:2022-11-10 发布日期:2022-11-21
通讯作者: 刘军涛(1822061948@qq.com)
作者简介:(1000289@nequ.edu.cn)
基金资助:
东北大学产学研战略合作项目(71971050)

Classification Method of Harmful Garbage Images Based on Improved EfficientNetV2

YUAN Hui-lin¹, LIU Jun-tao², HUANG Bi², HAN Zhen², FENG Chong²

1 College of Management,Northeastern University at Qinhuangdao,Qinhuangdao,Hebei 066004,China
2 College of Information Science and Engineering,Northeastern University,Shenyang 110819,China

Online:2022-11-10 Published:2022-11-21
About author:YUAN Hui-lin,born in 1969,Ph.D,professor.Her main research interests include modeling and optimization of complex systems,information retrieval,and artificial intelligence.
LIU Jun-tao,born in 1996,postgra-duate.His main research interests include machine learning and artificial intelligence.
Supported by:
Northeastern University “Industry University Research Strategic Cooperation Project”(71971050).

摘要/Abstract

摘要： 随着工业的迅速发展,垃圾产生的数量呈爆炸式上升,使得垃圾处理成为一个世界性的难题。我国政府对环境的关切也逐渐加深,不断推出了各种垃圾分类政策以及法律法规以监督市民进行垃圾分类。垃圾处理特别是有害垃圾处理,如电子废弃物等,如果处理不当,会对环境产生恶劣影响。有害垃圾图像数据具有数据质量低、图像不清晰的特点,采集来自不同设备的图像样本又有明显差异,因此有害垃圾图像处理面临巨大挑战,同时有害废弃物分类结果关系到环境污染问题,且目前产出的垃圾数量巨大,要求具有较高的处理速度和准确度。文中提出了一种基于卷积神经网络和注意力机制的垃圾图像分类方法。该方法不需要对输入的图像进行手工提取特征,通过深度学习模型框架,弥补传统图像处理算法的不足,实现对有害垃圾准确、高效的分类,可以较好地识别多种类型的有害垃圾。经实验验证,所提方法在harmful-waste数据集上的准确率达到97.46%,相比其他算法模型,其模型训练时间更短,性能更优。利用深度学习的方法,部署自动化垃圾分类模型,对于环境保护有重要意义。

关键词: 积神经网络, 注意力机制, 深度学习, 垃圾分类, 图像分类

Abstract: With the rapid development of industry,the amount of waste generated has also exploded,making waste disposal a worldwide problem.The Chinese government’s concern for the environment has gradually deepened,and various garbage classification policies and laws and regulations have been continuously introduced to supervise citizens’ garbage classification.Garbage disposal,especially hazardous garbage such as electronic waste,if it is improperly handled,will result in bad influence.Hazardous garbage image data has the characteristics of low data quality and unclear images.The image samples collected from different devices have obvious differences.Therefore,the image processing of hazardous garbage faces huge challenges.At the same time,the classification results of hazardous waste are related to environmental pollution issues.The amount of waste produced is huge,requiring high processing speed and accuracy.This paper proposes a garbage image classification method based on convolutional neural network and hybrid attention mechanism.This method does not need to manually extract features from the input image.Through the deep learning model framework,it overcomes the shortcomings of traditional image processing algorithms,achieves accurate and efficient classification of hazardous waste,and can better identify multiple types of hazardous waste.Experiment shows that the proposed method has an accuracy rate of 97.47% on the harmful-waste data set,the model training time is shorter,and its performance is better than other algorithm models.Using deep learning methods to deploy automated garbage classification models is of great significance to environmental protection.

Key words: Convolutional neural network, Attentional mechanism, Deep learning, Garbage classification, Image classification

中图分类号:

TP391

原慧琳, 刘军涛, 黄碧, 韩真, 冯宠. 基于改进EfficientNetV2的有害垃圾图像分类方法[J]. 计算机科学, 2022, 49(11A): 211100100-5. https://doi.org/10.11896/jsjkx.211100100

YUAN Hui-lin, LIU Jun-tao, HUANG Bi, HAN Zhen, FENG Chong. Classification Method of Harmful Garbage Images Based on Improved EfficientNetV2[J]. Computer Science, 2022, 49(11A): 211100100-5. https://doi.org/10.11896/jsjkx.211100100

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基于改进EfficientNetV2的有害垃圾图像分类方法

Classification Method of Harmful Garbage Images Based on Improved EfficientNetV2

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