Classification of waste materials with a smart garbage system for sustainable development: a novel model

Frontiers in Environmental Science

Environmental Science

Volkan Kaya¹ 

[1] null;

关键词: artificial intelligence;    deep learning;    transfer learning;    garbage;    waste classification;   

DOI  :  10.3389/fenvs.2023.1228732

 received in 2023-05-25, accepted in 2023-08-18,  发布年份 2023

来源: Frontiers

【 摘 要 】

In today’s conditions, where the human population is increasing, environmental pollution is also increasing around the world. One of the most important causes of environmental pollution is the waste materials in the garbage. Misuse of waste materials causes significant damage to both the environment and human health. With the developing technology, the recyclability of the raw material used in the production of waste materials significantly affects both the raw material needs of the countries and the energy savings. Therefore, many traditional activities are carried out in recycling facilities in order to reuse the waste materials that can be recycled in many countries. At the beginning of these activities is manual waste collection and pre-processing depending on the human workforce. This process poses a serious threat to both the environment and human health. For this reason, there is a need for a smart system that automatically detects and classifies the waste materials in the garbage. In this study, Xception, InceptionResNetV2, MobileNet, DenseNet121 and EfficientNetV2S deep learning methods based on artificial intelligence, which automatically classify the waste materials in the garbage, were used and in addition to these methods, Xception_CutLayer and InceptionResNetV2_CutLayer based on transfer learning techniques were proposed. The proposed methods and artificial intelligence-based deep learning methods were trained and tested with a dataset containing 6 different waste materials. According to the findings obtained as a result of training and testing, a classification success rate of 89.72% with the proposed Xception_CutLayer method and 85.77% with the InceptionResNetV2_CutLayer method, a better success rate was obtained than the other artificial intelligence-based methods discussed in the study.

【 授权许可】

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