Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model  

作　　者：Ehab Bahaudien Ashary Sahar Jambi Rehab B.Ashari Mahmoud Ragab 

机构地区：[1]Electrical and Computer Engineering Department,Faculty of Engineering,King Abdulaziz University,Jeddah,21589,Saudi Arabia [2]Information Systems Department,Faculty of Computing and Information Technology,King Abdulaziz University,Jeddah,21589,Saudi Arabia [3]Information Technology Department,Faculty of Computing and Information Technology,King Abdulaziz University,Jeddah,21589,Saudi Arabia [4]Department of Mathematics,Faculty of Science,Al-Azhar University,Naser City,Cairo,11884,Egypt

出　　处：《Computer Systems Science & Engineering》2023年第12期2953-2969,共17页计算机系统科学与工程（英文）

基　　金：funded by Institutional Fund Projects under Grant No. (IFPIP:557-135-1443).

摘　　要：Object Detection is the task of localization and classification of objects in a video or image.In recent times,because of its widespread applications,it has obtained more importance.In the modern world,waste pollution is one significant environmental problem.The prominence of recycling is known very well for both ecological and economic reasons,and the industry needs higher efficiency.Waste object detection utilizing deep learning(DL)involves training a machine-learning method to classify and detect various types of waste in videos or images.This technology is utilized for several purposes recycling and sorting waste,enhancing waste management and reducing environmental pollution.Recent studies of automatic waste detection are difficult to compare because of the need for benchmarks and broadly accepted standards concerning the employed data andmetrics.Therefore,this study designs an Entropy-based Feature Fusion using Deep Learning forWasteObject Detection and Classification(EFFDL-WODC)algorithm.The presented EFFDL-WODC system inherits the concepts of feature fusion and DL techniques for the effectual recognition and classification of various kinds of waste objects.In the presented EFFDL-WODC system,two major procedures can be contained,such as waste object detection and waste object classification.For object detection,the EFFDL-WODC technique uses a YOLOv7 object detector with a fusionbased backbone network.In addition,entropy feature fusion-based models such as VGG-16,SqueezeNet,and NASNetmodels are used.Finally,the EFFDL-WODC technique uses a graph convolutional network(GCN)model performed for the classification of detected waste objects.The performance validation of the EFFDL-WODC approach was validated on the benchmark database.The comprehensive comparative results demonstrated the improved performance of the EFFDL-WODC technique over recent approaches.

关 键 词：Object detection object classification waste management deep learning feature fusion 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]