Recycling Material Classification using Convolutional Neural Networks
Kaihua Liu, Xudong Liu · 2022
Recent years have witnessed continuous growth of the abilty of convolutional neural networks (CNNs) on solving image classification problems. More recently, CNNs have started to show promising results on classifying recyclable materials given their images. This work aims to showcase the effectiveness of CNN architectures for such classification task on our dataset combined from multiple public sources, where in total 12,873 images of recyclable materials are collected over four classes: glass, metal, paper, and plastic. To the best of our knowledge, our work is the first attempt to train and test CNN models on this dataset. To this end, we train a selection of CNN models, including a simple 8-layer CNN, AlexNet, VGGNet, and InceptionNet. Our empirical results show that VGG-16 (a VGGNet variant of 16 convolutional and pooling layers), combined with transfer learning, produces the best testing accuracy of 84.6%. Furthermore, we import this best model to implement a Raspberry Pi application and an Android application to demonstrate their potential for consumer and industrial usage.