Artificial Intelligence in Automated Sorting in Trash Recycling

• Bernardo S. Costa IFSuldeMinas
• Aiko C. S. Bernardes IFSuldeMinas
• Julia V. A. Pereira IFSuldeMinas
• Vitoria H. Zampa IFSuldeMinas
• Vitoria A. Pereira IFSuldeMinas
• Guilherme F. Matos IFTM
• Eduardo A. Soares UFSCAR
• Claiton L. Soares IFTM
• Alexandre F. Silva IFSuldeMinas

DOI: https://doi.org/10.5753/eniac.2018.4416

Resumo

A computer vision approach to classify garbage into recycling categories could be an efficient way to process waste. This project aims to take garbage waste images and classify them into four classes: glass, paper, metal and, plastic. We use a garbage image database that contains around 400 images for each class. The models used in the experiments are Pre-trained VGG-16 (VGG16), AlexNet, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and, Random Forest (RF). Experiments showed that our models reached accuracy around 93%.

Referências

[ABDOLI 2009] ABDOLI, S. (2009). Rfid application in municipal solid waste management system.

[Altman 1992] Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3):175–185.

[Antonisamy et al. 2017] Antonisamy, B., Premkumar, P. S., and Christopher, S. (2017). Principles and Practice of Biostatistics-E-book. Elsevier Health Sciences.

[Arebey et al. 2011] Arebey, M., Hannan, M., Basri, H., Begum, R. A., and Abdullah, H. (2011). Integrated technologies for solid waste bin monitoring system. Environmental monitoring and assessment, 177(1-4):399–408.

[Awe et al. 2017] Awe, O., Mengistu, R., and Sreedhar, V. (2017). Smart trash net: Waste localization and classification.

[Chowdhury and Chowdhury 2007] Chowdhury, B. and Chowdhury, M. U. (2007). Rfidbased real-time smart waste management system. In Telecommunication Networks and Applications Conference, 2007. ATNAC 2007. Australasian, pages 175–180. IEEE.

[Cortes and Vapnik 1995] Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3):273–297.

[Glouche and Couderc 2013] Glouche, Y. and Couderc, P. (2013). A smart waste management with self-describing objects. In The Second International Conference on Smart Systems, Devices and Technologies (SMART’13).

[Ho 1995] Ho, T. K. (1995). Random decision forests. In Document analysis and recognition, 1995., proceedings of the third international conference on, volume 1, pages 278–282. IEEE.

[Islam et al. 2012] Islam, M. S., Arebey, M., Hannan, M., and Basri, H. (2012). Overview for solid waste bin monitoring and collection system. In Innovation Management and Technology Research (ICIMTR), 2012 International Conference on, pages 258–262. IEEE.

[Krizhevsky et al. 2012] Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105.

[Parlikad and McFarlane 2007] Parlikad, A. K. and McFarlane, D. (2007). Rfid-based product information in end-of-life decision making. Control engineering practice, 15(11):1348–1363.

[Russakovsky et al. 2015] Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3):211–252.

[Simonyan and Zisserman 2014] Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[Sinha and Couderc 2012] Sinha, A. and Couderc, P. (2012). Using owl ontologies for selective waste sorting and recycling. In OWLED-2012.

[Swedberg 2008] Swedberg, C. (2008). Rfid helps reward consumers for recycling. RFID Journal, February.

[Thomas 2008] Thomas, V. M. (2008). Environmental implications of rfid. In Electronics and the Environment, 2008. ISEE 2008. IEEE International Symposium on, pages 1–5. IEEE.

[Yang and Thung ] Yang, M. and Thung, G. Classification of trash for recyclability status