Original scientific paper
https://doi.org/10.32985/ijeces.15.7.4
Real-Time Solid Waste Sorting Machine Based on Deep Learning
Imane Nedjar
orcid.org/0000-0002-5222-1193
; University of Tlemcen, Biomedical Engineering Laboratory Ecole Supérieure en Sciences Appliquées de Tlemcen, ESSA-Tlemcen, BP 165 RP Bel Horizon, Tlemcen 13000, Algeria
*
Mohammed M’hamedi
orcid.org/0009-0003-7899-2185
; University of Tlemcen, Faculty of Sciences, Department of Computer Science Ecole Supérieure en Sciences Appliquées de Tlemcen, ESSA-Tlemcen, BP 165 RP Bel Horizon, Tlemcen 13000, Algeria
Mokhtaria Bekkaoui
; University of Tlemcen, Manufacturing Engineering Laboratory of Tlemcen Ecole Supérieure en Sciences Appliquées de Tlemcen, ESSA-Tlemcen, BP 165 RP Bel Horizon, Tlemcen 13000, Algeria
* Corresponding author.
Abstract
The collection and separation of solid waste represent crucial stages in recycling. However, waste collection currently relies on static trash bins that lack customization to suit specific locations. By integrating artificial intelligence into trash bins, we can enhance their functionality. This study proposes the implementation of a sorting machine as an intelligent alternative to traditional trash bins. This machine autonomously segregates waste without human intervention, utilizing deep learning techniques and an embedded edge device for real-time sorting. Deploying a convolutional neural network model on a Raspberry Pi, the machine achieves solid waste identification and segregation via image recognition. Performance evaluation conducted on both the Stanford dataset and a dataset we created showcases the machine's high accuracy in detection and classification. Moreover, the proposed machine stands out for its simplicity and cost-effectiveness in implementation.
Keywords
Waste; deep leaning; raspberry pi; artificial intelligence; sorting machine;
Hrčak ID:
319162
URI
Publication date:
12.7.2024.
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