Garbage Classification Based On Deep Residual Weakly Supervised Learning Model

  IJETT-book-cover  International Journal of Recent Engineering Science (IJRES)          
  
© 2020 by IJRES Journal
Volume-7 Issue-3
Year of Publication : 2020
Authors : Zhijie Yang, Hongbin Huang
  10.14445/23497157/IJRES-V7I3P110

MLA 

MLA Style: Zhijie Yang, Hongbin Huang "Garbage Classification Based On Deep Residual Weakly Supervised Learning Model" International Journal of Recent Engineering Science 7.3(2020):47-51. 

APA Style: Zhijie Yang, Hongbin Huang. Garbage Classification Based On Deep Residual Weakly Supervised Learning Model  International Journal of Recent Engineering Science, 7(3),47-51.

Abstract
The realization of garbage classification has become a hot topic in society, but today’s garbage processing plants use the manual pipeline sorting method for waste sorting. This kind of work method has a harsh working environment, high labor intensity, low sorting efficiency. Moreover, for the treatment of large amounts of garbage, manual sorting can only sort out a minimal part, and the vast majority of the remaining garbage can only be landfilled, which undoubtedly brings significant waste of resources and environmental pollution risks. With the application and development of deep learning technology in computer vision, it is possible to use AI technology to automatically sort waste: using cameras to take pictures of waste and then detecting the type of waste in the pictures so that the machine can automatically sort waste. This can significantly save colossal labor costs and improve waste sorting efficiency. This paper is based on deep residual weakly supervised learning ResNext series networks to classify garbage images, researches and explores AI technology for garbage classification, and contributes to the whole society is garbage classification.

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Keywords
AI, Deep Learning, Garbage Classification, Manual sorting, ResNeXt