Category-aware Recycle Classification using Convolutional Neural Networks

Abstract

Generally, the filtration of non-recyclable garbage is conducted in order to make a material recycling process  efficient. However, this filtration task is very time-consuming and costly, since it is generally done manually. Thus,  it is significantly considered that an automation technique is necessary in solving this problem. There have been a  number of attempts to resolve this problem. The previous methods used the shallow convolutional neural network to  compose their network. However, these methods tend to display poor accuracy, which makes it impossible to be  applied in real-life situations. To solve this problem, I propose a novel category-aware recycle classification system.  The proposed system is composed of three models, which are garbage feature extractor, garbage classifier, and  recycle classifier. The input image is fed to the garbage feature extractor and then converted into feature maps. The  feature maps are then fed to each the garbage classifier and the recycle classifier. The garbage classifier predicts the  category of the input garbage image while the recycle classifier determines if the input is recyclable or not. Through  experiments, the proposed method outperforms the other state-of-the-art methods by a maximum 20.2% difference  in accuracy on Garbage Classification and Industrial and Residential Waste datasets.