基于深度学习的智能垃圾分类系统设计与实现
Design and Implementation of Intelligent Waste Sorting System Based on Deep Learning
余燕萍 1赵圣鲁1
作者信息
- 1. 江西信息应用职业技术学院,江西 南昌 330030
- 折叠
摘要
实施垃圾分类是一项具有深远意义的举措,它不仅能够节约宝贵的国家生态资源,而且深刻影响着居民的生活品质,成为衡量社会文明程度的重要标尺.随着垃圾分类工作在全国范围内的逐步推进和试行,越来越多的居民已经形成了垃圾分类的良好习惯,特别是在一些试点区域,管理工作也日渐规范化和精细化.然而,从更宏观的角度来看,目前社会上垃圾分类的普及意识和实际操作水平仍显不足,存在着诸多亟待解决的问题和挑战.与此同时,市场上的垃圾分类产品不仅功能相对单一,而且种类也较为有限,难以满足日益增长的多元化需求.鉴于此,开发了一个基于深度学习技术的智能垃圾分类系统,该系统涵盖了垃圾分类模型的训练、评估以及Web服务端开发等多个关键环节,旨在通过技术手段提升垃圾图片的识别准确率,从而改善现有垃圾分类软件的用户体验,促进垃圾分类工作的深入开展和全面普及.
Abstract
The implementation of garbage classification is a far-reaching measure,which can not only save valuable national ecological resources,but also profoundly affect the quality of life of residents,and become an important yardstick to measure the degree of social civilization.With the gradual promotion and trial of garbage classification throughout the country,more and more residents have formed a good habit of garbage classification,especially in some pilot areas,and the management work is increasingly standardized and refined.However,from a more macro point of view,the current social awareness and practical level of garbage classification is still insufficient,and there are many problems and challenges to be solved.At the same time,the garbage classification products on the market are not only relatively single in function,but also relatively limited in variety,which is difficult to meet the growing diversified needs.In view of this,an intelligent garbage classification system based on deep learning technology is developed,which covers several key links such as the training,evaluation and Web server development of garbage classification model,aiming to improve the recognition accuracy of garbage pictures through technical means,so as to improve the user experience of existing garbage classification software.Promote the in-depth development and comprehensive popularization of garbage classification work.
关键词
垃圾分类/深度学习/系统设计Key words
waste sorting/deep learning/system design引用本文复制引用
出版年
2024
NETL
NSTL
万方数据