Simple and Efficient Convolutional Neural Network for Trash Classification

• Autorzy: Tran, Binh-Giang , Nguyen, Duy-Linh
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

convolutional neural network; trash classification; trash recognition

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 33
• Strony: 255--260
• Opis fizyczny: Bibliogr. 20 poz., rys., tab., wykr.

Twórcy

autor

Tran, Binh-Giang

• Department of IT Vinh University of Technology and Education Nghe An, Vietnam,

autor

Nguyen, Duy-Linh

• School of Engineering, Electronic and Computer Engineering Universiry of Ulsan Ulsan, Korea,

Bibliografia

• [1] “The world’s growing municipal solid waste: trends and impacts.” Accessed: Jun. 24, 2022. [Online]. Available: https://iopscience.iop.org/article/10.1088/1748-9326/ab8659.
• [2] “Solid waste management.” Accessed: Jun. 24, 2022. [Online]. Available: https://www.worldbank.org/en/topic/urbandevelopment/brief/solid-waste-management.
• [3] “Humans on course to triple daily waste by 2100.” Accessed: Jun. 24, 2022. [Online]. Available: https://www.nbcnews.com/sciencemain/humans-course-triple-daily-waste-2100-8c11497511.
• [4] “Waste sorting.” Accessed: Jun. 24, 2022. [Online]. Available: https://en.wikipedia.org/wiki/Waste sorting.
• [5] G. Mangialardi, G. Trullo, F. Valerio, and A. Corallo, “Sustainability of a pneumatic refuse system in the metropolitan area: A case study in southern apulia region,” Procedia - Social and Behavioral Sciences, vol. 223, pp. 799–804, 2016. 2nd International Symposium “NEW METROPOLITAN PERSPECTIVES” - Strategic planning, spatial planning, economic programs and decision support tools, through the implementation of Horizon/Europe2020. ISTH2020, Reggio Calabria (Italy), 18-20 May 2016.
• [6] “Recycling facilities in the us.” Accessed: Jun. 24, 2022. [Online]. Available: https://ibisworld.com.
• [7] J. Yang, Z. Zeng, K. Wang, H. Zou, and L. Xie, “Garbagenet: A unified learning framework for robust garbage classification,” IEEE Transactions on Artificial Intelligence, vol. 2, no. 4, pp. 372–380, 2021.
• [8] A. Camero, J. Toutouh, J. Ferrer, and E. Alba, “Waste generation prediction in smart cities through deep neuroevolution,” in Smart Cities (S. Nesmachnow and L. Hernández Callejo, eds.), (Cham), pp. 192–204, Springer International Publishing, 2019.
• [9] T. Sheng, M. Shahidul Islam, N. Misran, M. H. Baharuddin, H. Arshad, M. R. Islam, M. Chowdhury, H. Rmili, and M. Islam, “An internet of things based smart waste management system using lora and tensorflow deep learning model,” IEEE Access, vol. PP, pp. 1–1, 08 2020.
• [10] Y. Liu, K.-C. Fung, W. Ding, H. Guo, T. Qu, and C. Xiao, “Novel smart wgaste sorting system based on image processing algorithms: Surf-bow and multi-class svm,” Computer and Information Science, vol. 11, p. 35, 06 2018.
• [11] “Modern convolutional neural networks.” Accessed: Jul. 10, 2022. [Online]. Available: https://d2l.ai/chapter convolutional-modern/index.html.
• [12] D. Thanawala, A. Sarin, and P. Verma, “An approach to waste segregation and management using convolutional neural networks,” in Advances in Computing and Data Sciences (M. Singh, P. K. Gupta, V. Tyagi, J. Flusser, T. Ören, and G. Valentino, eds.), (Singapore), pp. 139–150, Springer Singapore, 2020.
• [13] G. White, C. Cabrera, A. Palade, F. Li, and S. Clarke, “Wastenet: Waste classification at the edge for smart bins,” 2020.
• [14] H. Zheng and Y. Gu, “Encnn-upmws: Waste classification by a cnn ensemble using the upm weighting strategy,” Electronics, vol. 10, no. 4, 2021.
• [15] D. Ziouzios, D. Tsiktsiris, N. Baras, and M. Dasygenis, “A distributed architecture for smart recycling using machine learning,” Future Internet, vol. 12, no. 9, 2020.
• [16] K. Ahmad, K. Khan, and A. Al-Fuqaha, “Intelligent fusion of deep features for improved waste classification,” IEEE Access, vol. 8, pp. 96495–96504, 2020.
• [17] C. Shi, C. Tan, T. Wang, and L. Wang, “A waste classification method based on a multilayer hybrid convolution neural network,” Applied Sciences, vol. 11, no. 18, 2021.
• [18] M. Yang and G. Thung, “Classification of trash for recyclability status,” CS229 project report, vol. 2016, no. 1, p. 3, 2016.
• [19] “trashnet.” Accessed: Jul. 24, 2022. [Online]. Available: https://github.com/garythung/trashnet.
• [20] R. A. Aral, Ş. R. Keskin, M. Kaya, and M. Hacıömeroğlu, “Classification of trashnet dataset based on deep learning models,” in 2018 IEEE International Conference on Big Data (Big Data), pp. 2058–2062, 2018.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Identyfikatory

DOI

10.15439/2022R01