Open Access

ARTICLE

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

Ehab Bahaudien Ashary¹, Sahar Jambi², Rehab B. Ashari², Mahmoud Ragab^3,4,*

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

* Corresponding Author: Mahmoud Ragab. Email:

Computer Systems Science and Engineering 2023, 47(3), 2953-2969. https://doi.org/10.32604/csse.2023.041523

Received 26 April 2023; Accepted 13 June 2023; Issue published 09 November 2023

Abstract

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 and metrics. Therefore, this study designs an Entropy-based Feature Fusion using Deep Learning for Waste Object 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 fusion-based backbone network. In addition, entropy feature fusion-based models such as VGG-16, SqueezeNet, and NASNet models 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.

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Keywords

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