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Classification of Recyclable Materials Using Efficient Deep Learning Models and Benchmarking of GPU Performance

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1301)


One of the consequences of climate change and global warming results from excessive consumption of sources. To slow down global warming and increase energy saving, recycling, within the framework of waste management, needs to be widely implemented. Waste management and recycling is not only environmentally advantageous but also of great importance for a sustainable economy. Preferring smart systems instead of human workers is a socially important step that allows people to work in more welfare environments. Intelligent waste management approaches open up a major research area.

The main objective of the study is aimed to contribute to the efficient collection of recyclable materials from the end consumer, to optimize the collection process and to reduce the workload in the waste institution. In addition to the TrashNet dataset used in the previous classification of recyclable materials, an expanded dataset is collected, and a more advanced version is obtained. Data from three different classes, including glass, plastic, and metal waste, were collected and the current dataset was enhancement from 2527 to 6136. The new extended dataset is called TrashX. Therefore, not only the methods used in the literature have been improved, but also the convolutional neural network-based models used are tested. All results are evaluated according to performance criteria. In this research, 6 different recyclable waste classifications are made on a progressed dataset consisting of 6136 RGB images. Within the scope of this study, the largest dataset in the literature was created. For this purpose, high performance and robust models such as MobileNet, RecycleNet, and EfficientNet are offered. One of the most important factors of the study is that the performance of the models is evaluated in terms of time on different hardware. This benchmarking light on researchers to improve intelligence recycling and waste management systems.

Finally, the experiments are run to compare the performances of the methods for both TrashNet and the TrashX datasets. The experimental results demonstrate that EfficientNet-b3 efficiency 93.8% and 97.3% in terms of accuracy for Trashnet and TrashX datasets separately and thus it outperforms the many recent approaches for trash classification on both experimental datasets.


  • Waste management
  • Trash classification
  • Recyclable materials
  • Artificial intelligence
  • Computer vision
  • Deep learning

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  • DOI: 10.1007/978-3-030-66501-2_18
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  1. Williams, P.T.: Waste Treatment and Disposal. John Wiley & Sons, Hoboken (2005)

    CrossRef  Google Scholar 

  2. Palmer, J.A.: Environmental thinking in the early years: understanding and misunderstanding of concepts related to waste management. Environ. Educ. Res. 1(1), 35–45 (1995)

    CrossRef  Google Scholar 

  3. Amick, D.S.: Reflection on the origins of recycling: a paleolithic perspective. Lithic Technol. 39(1), 64–69 (2014)

    CrossRef  Google Scholar 

  4. Davidson, Cummings L.: Voluntary strategies in the environmental improvement: recycling as cooptation. J. Voluntary Action Res. 6(3–4), 153–160 (1977)

    CrossRef  Google Scholar 

  5. Alvarez-Ch ́avez, C.R., Edwards, S., Moure-Eraso, R., Geiser, K.: Sustainability of bio-based plastics: general comparative analysis and recommendations for improvement. J. Clean. Product. 23(1), 47–56 (2012)

    CrossRef  Google Scholar 

  6. Liu, C., Sharan, L., Adelson, E.H., Rosenholtz, R.: Exploring features in a Bayesian framework for material recognition. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 239–246. IEEE (2010)

    Google Scholar 

  7. Yang, M., Thung, G.: Classification of Trash for Recyclability Status, arXiv preprint (2016)

    Google Scholar 

  8. Thung, G.: Trashnet, GitHub repository (2016)

    Google Scholar 

  9. Bircanoğlu, C., Atay, M., Beşer, F., Genç, O., Kızrak, M.A.: RecycleNet: intelligent waste sorting using deep neural networks. In: IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) (2018)

    Google Scholar 

  10. Özkaya, U., Seyfi, L.: Fine-tuning models comparisons on garbage classification for recyclability. In: 2nd International Symposium on Innovative Approaches in Scientific Studies (2019)

    Google Scholar 

  11. Seredkin, A.V., Tokarev, M.P., Plohih, A., Gobyzov, O.A., Markovich, D.M.: Development of a method of detection and classification of waste objects on a conveyor for a robotic sorting system. J. Phys. Conf. Ser. 1359 (2019)

    Google Scholar 

  12. Vo, A.H., Son, L.H., Vo, M.T., Le, T.: A novel framework for trash classification using deep transfer learning. IEEE Access 7, 178631–178639 (2019)

    CrossRef  Google Scholar 

  13. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, arXiv preprint arXiv:1704.04861 (2017)

  14. Huang, G., Liu, Z., Weinberger, K.Q., Van Der Maaten, L.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, no. 2, (2017)

    Google Scholar 

  15. Chollet, F.: Xception: Deep Learning with Depthwise Separable Convolutions, arXiv preprint (2016)

    Google Scholar 

  16. Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Thirty-sixth International Conference on Machine Learning (ICML) (2019)

    Google Scholar 

  17. Google colaboratory (2017).

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The authors wish to acknowledge the support of the NVIDIA GPU Grant Program with the donation of the TITAN Xp GPU. The authors would also like to thank CRK Technology, which permits the data to be expanded to almost 2.5 times for this study.

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Correspondence to Merve Ayyüce Kızrak .

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Kömeçoğlu, Y., Kızrak, M.A. (2021). Classification of Recyclable Materials Using Efficient Deep Learning Models and Benchmarking of GPU Performance. In: Allahviranloo, T., Salahshour, S., Arica, N. (eds) Progress in Intelligent Decision Science. IDS 2020. Advances in Intelligent Systems and Computing, vol 1301. Springer, Cham.

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