RMGCS: Real-time multimodal garbage classification system for recyclability

Author:

Su Nan1,Lin Zhishuo1,You Wenlong1,Zheng Nan1,Ma Kun2

Affiliation:

1. School of Information Science and Engineering, University of Jinan, Jinan, China

2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China

Abstract

Management of garbage classification is a general term for a series of activities to sort, store and transport garbage into public resources according to certain regulations or standards. Current garbage classification systems have several drawbacks, such as inability to identify multiple garbage categories, and high dependence on the surrounding environment. To address these issues, this paper has proposed the Real Time Multi-Modal Garbage classification System (abbreviated as RMGCS). It consists of two sub systems: an indoor garbage classification applet (abbreviated as IGCA) and an outdoor garbage classification system (abbreviated as OGCS). IGCA provides users with three methods of garbage classification, and OGCS provides users with outdoor real-time multi-target garbage classification and can dynamically update the recognition model. RMGCS achieves real-time, accurate, and multimodal classification. Finally, the experiments with RMGCS show that our approaches are effective and efficient.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Comparative Analysis of Deep Learning Models and ResNet101-SVM Ensemble for Effective Garbage Classification;2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA);2023-12-21

2. Edge Computing-based Intelligent Garbage Classification and Recognition Application;2023 4th International Conference on Computers and Artificial Intelligence Technology (CAIT);2023-12-13

3. Raspberry Pi-based design of intelligent household classified garbage bin;Internet of Things;2023-12

4. A Systematic Review of Machine Learning Approaches for Trash Classification;2023 7th International Conference on Trends in Electronics and Informatics (ICOEI);2023-04-11

5. Towards Lightweight Neural Networks for Garbage Object Detection;Sensors;2022-09-30

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