The problem of segregating recyclable waste is fairly daunting for many countries. This article presents an approach for image based classification of plastic waste using one-shot learning techniques. The proposed approach exploits discriminative features generated via the siamese and triplet loss convolutional neural networks to help differentiate between 5 types of plastic waste based on their resin codes. The approach achieves an accuracy of 99.74% on the WaDaBa Database
@article{arxiv.2009.13953,
title = {One-Shot learning based classification for segregation of plastic waste},
author = {Shivaank Agarwal and Ravindra Gudi and Paresh Saxena},
journal= {arXiv preprint arXiv:2009.13953},
year = {2020}
}
Comments
Accepted in The International Conference on Digital Image Computing: Techniques and Applications, 2020