Deep based learning: ebin sorting system development

Dang Vu Kim Ky, Nguyen Huynh Thong, Au Thuy An, Pham Tan Hung
Author affiliations


  • Dang Vu Kim Ky Faculty of Electrical and Electronics Engineering, Ho Chi Minh city University of Technology, Ho Chi Minh city, Vietnam; Vietnam National University Ho Chi Minh City, Ho Chi Minh city, Vietnam
  • Nguyen Huynh Thong Vietnam National University Ho Chi Minh city, Ho Chi Minh city, Vietnam; Faculty of Geological and Petroleum Engineering, Ho Chi Minh city University of Technology, Ho Chi Minh city, Vietnam
  • Au Thuy An Vietnam National University Ho Chi Minh city, Ho Chi Minh city, Vietnam; Faculty of Geological and Petroleum Engineering, Ho Chi Minh city University of Technology, Ho Chi Minh city, Vietnam
  • Pham Tan Hung Faculty of Geological and Petroleum Engineering, Ho Chi Minh city University of Technology, Ho Chi Minh city, Vietnam; Faculty of Environment and Natural Resources, Ho Chi Minh city University of Technology, Ho Chi Minh city, Vietnam



Convolution neural network, deep learning, trash classification, computer vision, tensorflow object detection API.


Vietnam was the fifth country that dumped more plastic into the oceans than the rest of the world combined. The country produces an average of 25.5 million tons of waste per year, of which 75% is buried. Several burial sites in major cities such as Hanoi, Ho Chi Minh city, and Da Nang city are overloaded and negatively affecting citizens’ lives. Therefore, sorting at source would significantly reduce soil resources and groundwater pollution and save money on collection costs, transportation, treatment, and easing the pressure on the landfills. Using computer vision may help classify easily recyclable trash into different categories based on its material. This paper proposes an experiment on a customed Convolution Neural Network (CNN) based waste detector for three classes of trash: Plastic bottles, Cans, and Glass. The study used up to 900 hand-collected image data separated into 500 for analysis and 400 remains for assessing the result. All trashed objects used for generating data were collected around the Ho Chi Minh city University of Technology (HCMUT, Vietnam). The Deep Learning model used in this research, named SSD MobileNet V2, is part of the open-source Tensorflow Object Detection API. The system gave the result, the relative match percentage of different amounts of data we fed the model. The system showed linear relativity between the amount of data trained AI100, AI200, AI300, AI400, AI500, and the mean Average Precision (mAP) when testing the system. From this on, the study conducted another experiment to ensure that the performance of the trained model would meet expectations, AI401. In this experiment, the system showed over 80% inaccuracy overall. By back-analysis, this paper would be a good vision for researchers to study and enhance the system’s accuracy to serve the purpose of trash classification in innovative trash bin applications.


Download data is not yet available.


Metrics Loading ...


DONRE (Department of Natural Resource and Management)-Ho Chi Minh City, 2014. The report on overview of the SWM system of HCMC, Vietnam.

Verma, R. L., Borongan, G., and Memon, M., 2016. Municipal solid waste management in Ho Chi Minh City, Viet Nam, current practices and future recommendation. Procedia Environmental Sciences, 35, 127–139.

Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menendez, R., Bhardwaj, P., Sharma, S., and Sarker, I. H., 2020. AquaVision: Automating the detection of waste in water bodies using deep transfer learning. Case Studies in Chemical and Environmental Engineering, 2, 100026.

Bai, J., Lian, S., Liu, Z., Wang, K., and Liu, D., 2018. Deep learning based robot for automatically picking up garbage on the grass. IEEE Transactions on Consumer Electronics, 64(3), 382–389.

Costa, B. S., Bernardes, A. C., Pereira, J. V., Zampa, V. H., Pereira, V. A., Matos, G. F., Soares, E. A., Soares, C. L., and Silva, A. F., 2018. Artificial intelligence in automated sorting in trash recycling. In Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (pp. 198–205). SBC.

Vo, A. H., Vo, M. T., and Le, T., 2019. A novel framework for trash classification using deep transfer learning. IEEE Access, 7, 178631–178639.

Yu, J., Jiang, Y., Wang, Z., Cao, Z., and Huang, T., 2016. Unitbox: An advanced object detection network. In Proceedings of the 24th ACM international conference on Multimedia (pp. 516–520).

VMENR (Vietnam Ministry of Environment and Natural Resources), 2014. Canadian International Development Agency, Vietnam Environment Monitor: Solid Waste, Vietnam.

Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A., 2010. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2), 303–338.

Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L., 2014. Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740–755). Springer, Cham.

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L., 2015. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211–252.

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X., 2016. TensorFlow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265–283).

Shi, L., Zhong, H., Xie, T., and Li, M., 2011. An empirical study on evolution of API documentation. In International Conference on Fundamental Approaches To Software Engineering (pp. 416–431). Springer, Berlin, Heidelberg.

Blengini, G. A., Busto, M., Fantoni, M., and Fino, D., 2012. Eco-efficient waste glass recycling: Integrated waste management and green product development through LCA. Waste management, 32(5), 1000–1008.

Vellini, M., and Savioli, M., 2009. Energy and environmental analysis of glass container production and recycling. Energy, 34(12), 2137–2143.




How to Cite

Dang Vu, K. K., Nguyen Huynh, T., Au Thuy, A., & Pham Tan, H. (2022). Deep based learning: ebin sorting system development. Vietnam Journal of Marine Science and Technology, 22(1), 85–93.