One-Shot learning based classification for segregation of plastic waste

• URL: http://arxiv.org/abs/2009.13953v1
• Date: Tue, 29 Sep 2020 12:16:50 GMT
• Title: One-Shot learning based classification for segregation of plastic waste
• Authors: Shivaank Agarwal, Ravindra Gudi, Paresh Saxena
• Abstract summary: 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.
• Score: 0.8103046443444949
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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