# Application of Computer Vision Techniques for Segregation of PlasticWaste based on Resin Identification Code

arXiv: https://arxiv.org/abs/2011.07747 · HTML: https://mlsanity.com/paper/2011.07747/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Machine Learning of polymer types from the spectral signature of Raman spectroscopy microplastics data](https://mlsanity.com/paper/2201.05445/) (2022)

## Abstract (arXiv)

This paper presents methods to identify the plastic waste based on its resin identification code to provide an efficient recycling of post-consumer plastic waste. We propose the design, training and testing of different machine learning techniques to (i) identify a plastic waste that belongs to the known categories of plastic waste when the system is trained and (ii) identify a new plastic waste that do not belong the any known categories of plastic waste while the system is trained. For the first case,we propose the use of one-shot learning techniques using Siamese and Triplet loss networks. Our proposed approach does not require any augmentation to increase the size of the database and achieved a high accuracy of 99.74%. For the second case, we propose the use of supervised and unsupervised dimensionality reduction techniques and achieved an accuracy of 95% to correctly identify a new plastic waste.