Simple and Efficient Convolutional Neural Network for Trash Classification
Binh-Giang Tran, Duy-Linh Nguyen
Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 255–260 (2022)
Abstract. Strong economic and city developments have given a great amount of trash. Trash is made continuously from families, public and commercial areas, construction places, hospitals, etc. The enlarging trash amount is a much more serious problem than climate change, and the cost of trash treatment will be a big burden to countries in the world.One of the effective trash treatment measures is to separate trash right from its source, especially domestic trash. The countries have applied many trash classification systems, but the requirements for infrastructure, implementation, and operation are quite complicated. In order to help people easily sort household trash at home, this paper proposes a simple convolutional neural network for trash classification. The network is trained and evaluated on the TrashNet dataset with an accuracy of 90.71\\%. In addition, this work also tests in real-time on low-computation devices such as CPU-based personal computer and Jetson Nano devices.
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