International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
권호 표지
DOI QR코드

DOI QR Code

Waste Classification by Fine-Tuning Pre-trained CNN and GAN

  • 투고 : 2021.08.05
  • 발행 : 2021.08.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Waste accumulation is becoming a significant challenge in most urban areas and if it continues unchecked, is poised to have severe repercussions on our environment and health. The massive industrialisation in our cities has been followed by a commensurate waste creation that has become a bottleneck for even waste management systems. While recycling is a viable solution for waste management, it can be daunting to classify waste material for recycling accurately. In this study, transfer learning models were proposed to automatically classify wastes based on six materials (cardboard, glass, metal, paper, plastic, and trash). The tested pre-trained models were ResNet50, VGG16, InceptionV3, and Xception. Data augmentation was done using a Generative Adversarial Network (GAN) with various image generation percentages. It was found that models based on Xception and VGG16 were more robust. In contrast, models based on ResNet50 and InceptionV3 were sensitive to the added machine-generated images as the accuracy degrades significantly compared to training with no artificial data.

키워드

참고문헌

  1. H. Wang, "Garbage recognition and classification system based on convolutional neural network vgg16," in 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEM-CSE). IEEE, 2020, pp. 252-255.
  2. Y. Liao, "A web-based dataset for garbage classification based on shanghai's rule," International Journal of Machine Learning and Computing, vol. 10, no. 4, 2020.
  3. M. Zeng, X. Lu, W. Xu, T. Zhou, and Y. Liu, "Public garbagenet: A deep learning framework for public garbage classification," in 2020 39th Chinese Control Conference (CCC). IEEE, 2020, pp. 7200-7205.
  4. L. Cao and W. Xiang, "Application of convolutional neural network based on transfer learning for garbage classification," in 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC).IEEE, 2020, pp. 1032-1036.
  5. R. Sidharth, P. Rohit, S. Vishagan, R. Karthika, and M. Ganesan, "Deep learning based smart garbage classifier for effective waste management," in2020 5th Inter-national Conference on Communication and ElectronicsSystems (ICCES). IEEE, 2020, pp. 1086-1089.
  6. M. Yang and G. Thung, "Classification of trash for recyclability status," CS229 Project Report, vol. 2016,2016.
  7. R. A. Aral, S. R. Keskin, M. Kaya, and M. Haciomeroglu, "Classification of trashnet dataset based on deep learning models," in 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018, pp. 2058-2062.
  8. S. L. Rabano, M. K. Cabatuan, E. Sybingco, E. P. Dadios, and E. J. Calilung, "Common garbage classification using mobilenet," in2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM). IEEE, 2018, pp. 1-4.
  9. U. Ozkaya and L. Seyfi, "Fine-tuning models comparisons on garbage classification for recyclability," arXivpreprint arXiv:1908.04393, 2019.
  10. A. H. Vo, M. T. Vo, T. Leet al., "A novel framework for trash classification using deep transfer learning," IEEEAccess, vol. 7, pp. 178 631-178 639, 2019.
  11. S. Albawi, T. A. Mohammed, and S. Al-Zawi, "Understanding of a convolutional neural network," in2017International Conference on Engineering and Technology(ICET). Ieee, 2017, pp. 1-6.
  12. I. Kandel and M. Castelli, "Transfer learning with convolutional neural networks for diabetic retinopathy image classification. a review," Applied Sciences, vol. 10, no. 6,p. 2021, 2020. https://doi.org/10.3390/app10062021
  13. L. Torrey and J. Shavlik, "Transfer learning," in Handbook of research on machine learning applications and trends: algorithms, methods, and techniques. IGI Global,2010, pp. 242-264.
  14. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009, pp. 248-255.
  15. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, andZ. Wojna, "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp.2818-2826.
  16. F. Chollet, "Xception: Deep learning with depthwise separable convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition,2017, pp. 1251-1258.
  17. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXivpreprint arXiv:1409.1556, 2014.
  18. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
  19. I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu,D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial networks," arXiv preprintarXiv:1406.2661, 2014.
  20. T. Karras, S. Laine, and T. Aila, "A style-based generator architecture for generative adversarial networks," in Proceedings of the IEEE/CVF Conference on ComputerVision and Pattern Recognition, 2019, pp. 4401-4410.
  21. G. Thung and M. Yang, "Trashnet," GitHub repository,2016.
  22. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury,G. Chanan, T. Killeen, Z. Lin, N. Gimelshein,L.Antiga, A.Desmaison, A.Kopf, E.Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, "Pytorch: An imperative style, high-performance deep learning library," in Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch e-Buc, E. Fox, and R. Garnett, Eds.Curran Associates, Inc., 2019, pp. 8024-8035. [Online]. Available: http://papers.neurips.cc/paper/9015-pytorch-animperative-style-high-performance-deep-learninglibrary.pdf
  23. Z. Hussain, F. Gimenez, D. Yi, and D. Rubin, "Differential data augmentation techniques for medical imaging classification tasks," in AMIA Annual Symposium Proceedings, vol. 2017.American Medical InformaticsAssociation, 2017, p. 979.
  24. A. Mikolajczyk and M. Grochowski, "Data augmentation for improving deep learning in image classification problem," in2018 international interdisciplinary PhD workshop (IIPhDW). IEEE, 2018, pp. 117-12