A Survey on Existing Convolutional Neural Networks and Waste Management Techniques and an Approach to Solve Waste Classification Problem Using Neural Networks

  • Tejashwini Hiremath
  • S. RajarajeswariEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 906)


In India, waste management has become one of the major crises with population explosion, coupled with improved lifestyle of people, results in increased generation of solid wastes in urban as well as rural areas of the country. It is well known that waste management policies, as they exist now, are not sustainable in the long term. Thus, waste management is undergoing drastic change to offer more options that are more sustainable. Most of the landfills are becoming full of waste in which most part is reusable and leading to spreading of disease damaging human body and leading to unpleasant air and only 5% of whole waste is actual waste. The government of Karnataka mandated system of 2 BIN 1 BAG to be adapted at every households in Bangalore, and 2 BIN 1 BAG is a color-coded system consisting of green bin which holds garden waste, and the wastes that are compostable, reject waste can be thrown in red bin, and finally a big category called as reusable bag which holds recyclable waste. Segregation of waste at source is best solution and should be done properly. Types of waste need to be remembered by members of home in order to put them to proper bins, and this may lead to human error. So our solution can answer this in good way, what if you just click picture of waste material and application says to which category it belongs. A convolutional neural network is trained with images of waste materials, and model can be inferred by giving waste-material image as input and get the perfect category of waste material in a second. This helps society in dealing with prime problem of segregating waste materials at source.


Solid waste management (SWM) 2 BIN 1 BAG system Convolution neural networks (CNN) Stochastic gradient descent (SGD) Neural networks (NN) 


  1. 1.
    Goris, L. M., Harish, M. T., & Bhavani, R. R. (2017). A system design for solid waste management: A case study of an implementation in Kerala. IEEE, 2017.Google Scholar
  2. 2.
    Khandare, M. J., & Khandare, S. M. (2016). Waste materials and management in civil, mechanical and electronics engineering. In International Conference and Workshop on Electronics and Telecommunication Engineering 2016.Google Scholar
  3. 3.
    Nizarudin, S., & Deepak, B., Thermo-catalytic degradation: Solution for plastic waste management in Kerala. In IEEE R10.B. Smith, “An approach to graphs of linear forms (Unpublished work style)” (unpublished).Google Scholar
  4. 4.
    Ramesh, S., Usman, A., Usman, A., & Divakar, B. P. (2013). Municipal solid waste management in Bangalore and the concept of mini biogas plant in urban localities. IEEE, 2013.Google Scholar
  5. 5.
    Siddappaji., Sujatha, K., & Radha, R. C. (2016). Technologies for segregation and management of solid waste: A review. IEEE, 2016.Google Scholar
  6. 6.
    Milić, P., & Jovanović, M. (2011). The advanced system for dynamic vehicle routing in the process of waste collection.Google Scholar
  7. 7.
    Peltola, T., & Mäkinen, S. J. (2015). Identifying critical technology actors in waste flow management.Google Scholar
  8. 8.
    Jain, A., Developments and evaluation of existing policies and regulations for E-waste in India.Google Scholar
  9. 9.
    Krizhevsky, A., Sutskever, I., & Hinton, G. E., Image net classification with deep convolutional neural networks. In NIPS 2012: Neural Information Processing Systems, Lake Tahoe, Nevada.Google Scholar
  10. 10.
    Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. Published at ECCV 2014 and Springer International Publishing Switzerland 2014.Google Scholar
  11. 11.
    Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. Published as a conference paper at ICLR 2015.Google Scholar
  12. 12.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. CVPR 2015.Google Scholar
  13. 13.
    Hu, J., Momenta, & Shen, L., Squeeze-and-excitation networks. arXiv:1709.01507v1 [cs.CV] 5 Sep 2017.
  14. 14.
    Dahl, G. E., Sainath, T. N., & Hinton, G. E., Improving deep neural networks for LVCSR using rectified linear units and dropout. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringRamaiah Institute of TechnologyBangaloreIndia

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