Recycle Resources Classification using Image Recognition for Recycling Education Support System

Abstract

The purpose of this study is to develop an educational application for children to identify recyclable resources from waste images taken by mobile terminals. CNN-based transfer learning was used in the classification of images taken from actual household waste. We developed the original dataset ``STE'' and compared to TrashNet dataset. The classification accuracy varied depending on the types of waste, where ``paper'' or ``plastic'' waste was found to have relatively high accuracy in our STE dataset, particularly after background removal.