Segregation of Plastic Wastes from Landfills from Infrared Imaging Using Deep Learning Approaches

Authors

  • S. Leninisha
  • S. Dipika
  • A. Archana
  • V. Aishwarya

Abstract

Management of wastes is a great threat to environment protection. The waste composition may contain contaminated, assorted mixture of plastics making its segregation very challenging. Both manpower and time are needed more for sorting out the waste. They can be sorted out in enormous types of techniques. Here comes, Image processing which can be a very efficient way to process waste materials efficiently from the garbage. One of the prominent recent times deep learning algorithm could be convolution neural networks (CNN) that can effectively scrutinize the plastics from landfills and dumps. This paper provides an automated system using Infrared imaging and neural networks for detection and segregation of plastic waste. Previous researches have been more about single object recognition and classification. Thus the work proposed identifies plastics present in the mixture of wastes by analyzing the infrared images using Alex net. Hence the result can pave a way to address the shortcomings of current plastic sorting technologies in terms of reliability and efficiency..

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Published

2020-04-09

Issue

Section

Articles