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A Deep Trash Classification Model on Raspberry Pi 4

Thien Khai Tran1, Kha Tu Huynh2,*, Dac-Nhuong Le3, Muhammad Arif4, Hoa Minh Dinh1

1 Ho Chi Minh City University of Foreign Languages and Information Technology, Ho Chi Minh City, 700000, Vietnam
2 International University, Ho Chi Minh City, Vietnam-Vietnam National University, Ho Chi Minh City, 70000, Vietnam
3 Faculty of Information Technology, Haiphong University, Haiphong, 180000, Vietnam
4 Department of Computer Science and Information Technology, University of Lahore, Lahore, Pakistan

* Corresponding Author: Kha Tu Huynh. Email: email

Intelligent Automation & Soft Computing 2023, 35(2), 2479-2491.


Environmental pollution has had substantial impacts on human life, and trash is one of the main sources of such pollution in most countries. Trash classification from a collection of trash images can limit the overloading of garbage disposal systems and efficiently promote recycling activities; thus, development of such a classification system is topical and urgent. This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time. An image dataset is first augmented to enhance the images before classifying them as either inorganic or organic trash. The deep learning–based ResNet-50 model, an improved version of the ResNet model, is used to classify trash from the dataset of trash images. The experimental results, which are tested both on the dataset and in real time, show that ResNet-50 had an average accuracy of 96%, higher than that of related models. Moreover, integrating the classification module into a Raspberry Pi computer, which controlled the trash bin slide so that garbage fell into the appropriate bin for inorganic or organic waste, created a complete trash classification system. This proves the efficiency and high applicability of the proposed system.


Cite This Article

APA Style
Tran, T.K., Huynh, K.T., Le, D., Arif, M., Dinh, H.M. (2023). A deep trash classification model on raspberry pi 4. Intelligent Automation & Soft Computing, 35(2), 2479-2491.
Vancouver Style
Tran TK, Huynh KT, Le D, Arif M, Dinh HM. A deep trash classification model on raspberry pi 4. Intell Automat Soft Comput . 2023;35(2):2479-2491
IEEE Style
T.K. Tran, K.T. Huynh, D. Le, M. Arif, and H.M. Dinh "A Deep Trash Classification Model on Raspberry Pi 4," Intell. Automat. Soft Comput. , vol. 35, no. 2, pp. 2479-2491. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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