Open Access

ARTICLE

Deep Consensus Network for Recycling Waste Detection in Smart Cities

Manar Ahmed Hamza^1,*, Hanan Abdullah Mengash², Noha Negm³, Radwa Marzouk², Abdelwahed Motwakel¹, Abu Sarwar Zamani¹

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, 16278, AlKharj, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Computer Science, College of Science and Arts, King Khalid University, Mahayil Asir, 62529,Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email:

Computers, Materials & Continua 2023, 75(2), 4191-4205. https://doi.org/10.32604/cmc.2023.027050

Received 10 January 2022; Accepted 04 March 2022; Issue published 31 March 2023

Abstract

Recently, urbanization becomes a major concern for developing as well as developed countries. Owing to the increased urbanization, one of the important challenging issues in smart cities is waste management. So, automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management. Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials. This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection (DCNWO-RWOD) in Smart Cities. The goal of the DCNWO-RWOD technique intends to properly identify and classify the objects into recyclable and non-recyclable ones. The proposed DCNWO-RWOD technique involves the design of deep consensus network (DCN) to detect waste objects in the input image. For improving the overall object detection performance of the DCN model, the whale optimization algorithm (WOA) is exploited. Finally, Naïve Bayes (NB) classifier is used for the classification of detected waste objects into recyclable and non-recyclable ones. The performance validation of the DCNWO-RWOD technique takes place using the open access dataset. The extensive comparative study reported the enhanced performance of the DCNWO-RWOD technique interms of several measures.

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Keywords

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