One-Shot learning based classification for segregation of plastic waste
This addresses plastic waste segregation, a critical environmental issue, but is incremental as it applies known one-shot learning techniques to a specific domain.
The paper tackled the problem of classifying plastic waste by resin code using one-shot learning with siamese and triplet loss CNNs, achieving an accuracy of 99.74% on the WaDaBa Database.
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