A New Paradigm for Waste Classification Based on YOLOv5

作     者：Mohammed SAJID Nimali T MEDAGEDARA 

作者机构：Department of Mechanical EngineeringUniversity of West of England Department of Mechanical En-gineeringFaculty of Engineering TechnologyThe Open University of Sri LankaNugegodaSri Lanka 

出 版 物：《Instrumentation》 (仪器仪表学报（英文版）)

年 卷 期：2021年第8卷第4期

页      面：9-17页

学科分类：083002[工学-环境工程] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

主　　题：Waste Classification YOLOv5 

摘      要：Classification of garbage is of paramount importance prior to process them to categorise physically and this process helps to manage wastes by maintaining pollution free *** systems that have capability segregate garbage are on the rise,but efficient and accurate segmentation with recognition mechanisms draw the attention of researchers.A computer vision approach for classifying garbage into respective recyclable categories could be one of the effective and economical ways of processing *** project mainly focused on capturing real-time images of a single piece of garbage and classifying it into three divisions:paper,or metal,or biodegradable(food)*** garbage class contains around 2000 images obtained from an open-source dataset and images collected from Google and personally collected custom *** developed intelligent models provide the effectiveness of the machine and deep learning in classification with structural and non-structural *** model used was a Convolutional Neural Network(CNN)named *** project showcased vision based approach capable of maintaining an accuracy of 61%.The CNN was not trained to its maximum capacity due to the difficulty of finding optimal hyperparameters,as most of the images were gathered from Google Images.