Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

作     者：Mesfer Al Duhayyim Taiseer Abdalla Elfadil Eisa Fahd NAl-Wesabi Abdelzahir Abdelmaboud Manar Ahmed Hamza Abu Sarwar Zamani Mohammed Rizwanullah Radwa Marzouk 

作者机构：Department of Natural and Applied SciencesCollege of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems-Girls SectionKing Khalid UniversityMahayil62529Saudi Arabia Department of Computer ScienceKing Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Information SystemsKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self DevelopmentPreparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information SystemsCollege of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of MathematicsFaculty of ScienceCairo UniversityGiza12613Egypt Corresponding Author:Manar Ahmed Hamza.Email:ma.hamza@psau.edu.sa 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第71卷第6期

页      面：5699-5715页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 1/282/42) This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-Track Research Funding Program 

主　　题：Smart cities deep reinforcement learning computer vision image classification object detection waste management 

摘      要：The Smart City concept revolves around gathering real time data from citizen,personal vehicle,public transports,building,and other urban infrastructures like power grid and waste disposal *** understandings obtained from the data can assist municipal authorities handle assets and services *** the same time,the massive increase in environmental pollution and degradation leads to ecological imbalance is a hot research ***,the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of *** of the commonly available wastes are paper,paper boxes,food,glass,*** order to classify the waste objects,computer vision based solutions are cost effective to separate out the waste from the huge dump of garbage and *** to the recent developments of deep learning(DL)and deep reinforcement learning(DRL),waste object classification becomes possible by the identification and detection of *** this aspect,this paper designs an intelligence DRL based recycling waste object detection and classification(IDRL-RWODC)model for smart *** goal of the IDRLRWODC technique is to detect and classify waste objects using the DL and DRL *** IDRL-RWODC technique encompasses a twostage process namely Mask Regional Convolutional Neural Network(Mask RCNN)based object detection and DRL based object *** addition,DenseNet model is applied as a baseline model for the Mask RCNN model,and a deep Q-learning network(DQLN)is employed as a ***,a dragonfly algorithm(DFA)based hyperparameter optimizer is derived for improving the efficiency of the DenseNet *** order to ensure the enhanced waste classification performance of the IDRL-RWODC technique,a series of simulations take place on benchmark dataset and the experimental results pointed out the better performance over the recent techniques