A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
YOLOv5 Model-Based Real-Time Recyclable Waste Detection and Classification System
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.
YOLOv5 Model-Based Real-Time Recyclable Waste Detection and Classification System
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.
YOLOv5 Model-Based Real-Time Recyclable Waste Detection and Classification System
Lect. Notes in Networks, Syst.
Ben Ahmed, Mohamed (editor) / Boudhir, Anouar Abdelhakim (editor) / El Meouche, Rani (editor) / Karaș, İsmail Rakıp (editor) / Rahim, Leena Ardini Abdul (author) / Abidin, Nor Afirdaus Zainal (author) / Aminuddin, Raihah (author) / Samah, Khyrina Airin Fariza Abu (author) / Ibrahim, Asma Zubaida Mohamed (author) / Yusoh, Syarifah Diyanah (author)
The Proceedings of the International Conference on Smart City Applications ; 2023 ; Paris, France
2024-02-20
11 pages
Article/Chapter (Book)
Electronic Resource
English
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