Design and implementation of garbage classification and detection system based on YOLOv8

Quanfu Wang; Bin Wen

doi:10.1117/12.3015103

4 March 2024 Design and implementation of garbage classification and detection system based on YOLOv8

Quanfu Wang, Bin Wen

Author Affiliations +

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 129811Y (2024) https://doi.org/10.1117/12.3015103
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

Garbage classification can be seen everywhere in today's society, but due to the changes of the times, the awareness that different types of waste should go into different types of bins is not widespread among most people. Even some people don't even know what kinds of garbage there are. Therefore, it is very important to identify garbage. In order to speed up training and recognition, the author improved the YOLOv8 algorithm newly released this year, and at the same time used crawlers to crawl thousands of pictures of different types of garbage on the Internet for training. The author obtained the model with the highest accuracy by increasing the number of epochs and adjusting the batch.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Quanfu Wang and Bin Wen "Design and implementation of garbage classification and detection system based on YOLOv8", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 129811Y (4 March 2024); https://doi.org/10.1117/12.3015103

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KEYWORDS

Data modeling

Education and training

Object detection

Detection and tracking algorithms

Classification systems

Design and modelling

Data conversion

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