卷积神经网络
危险废物
过程(计算)
人工神经网络
支持向量机
计算机科学
城市固体废物
环境污染
人工智能
深度学习
提取器
工程类
废物管理
工艺工程
环境科学
操作系统
环境保护
作者
Adedeji Olugboja,Zenghui Wang
标识
DOI:10.1016/j.promfg.2019.05.086
摘要
The accumulation of solid waste in the urban area is becoming a great concern, and it would result in environmental pollution and may be hazardous to human health if it is not properly managed. It is important to have an advanced/intelligent waste management system to manage a variety of waste materials. One of the most important steps of waste management is the separation of the waste into the different components and this process is normally done manually by hand-picking. To simplify the process, we propose an intelligent waste material classification system, which is developed by using the 50-layer residual net pre-train (ResNet-50) Convolutional Neural Network model which is a machine learning tool and serves as the extractor, and Support Vector Machine (SVM) which is used to classify the waste into different groups/types such as glass, metal, paper, and plastic etc. The proposed system is tested on the trash image dataset which was developed by Gary Thung and Mindy Yang, and is able to achieve an accuracy of 87% on the dataset. The separation process of the waste will be faster and intelligent using the proposed waste material classification system without or reducing human involvement.
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