Citation

Abdul Rahim, Leena Ardini and Zainal Abidin, Nor Afirdaus and Aminuddin, Raihah and Abu Samah, Khyrina Airin Fariza and Mohamed Ibrahim, Asma Zubaida and Yusoh, Syarifah Diyanah and Mohd Nasir, Siti Diana Nabilah (2024) YOLOv5 Model-Based Real-Time Recyclable Waste Detection and Classification System. Lecture Notes in Networks and Systems, 906. pp. 44-54. ISSN 2367-3370

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Abstract

Emerging nations, driven by population growth and rapid urbanization, generate significant waste. Inadequate waste management systems prevail in many countries, including Malaysia, due to a lack of understanding and insufficient infrastructure. Despite poor waste management, there needs to be an automated classification system, leading to time-consuming manual recycling processes. The project aims to develop a real-time waste identification and classification system. The project’s objectives are: 1) design a prototype using a web application and a real-time video platform to detect and categorize recyclable waste; 2) develop the prototype utilizing the YOLOv5 model; and 3) test the model’s accuracy. In the real-time video environment, the system can identify the type of waste and the corresponding recycle bin colors for proper disposal. The model achieved an accuracy rate of 86.25% in identifying and detecting the waste.

Item Type:	Article
Uncontrolled Keywords:	YOLOv5, recycle, deep learning, image processing
Subjects:	Q Science > Q Science (General)
Divisions:	Faculty of Business (FOB)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Apr 2024 02:05
Last Modified:	03 Apr 2024 02:05
URII:	http://shdl.mmu.edu.my/id/eprint/12304

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