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One-Shot learning based classification for segregation of plastic waste
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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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Cited by 1 Pith paper
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Detailed Evaluation of Modern Machine Learning Approaches for Optic Plastics Sorting
An evaluation of RGB-based deep learning models for plastic sorting shows they often attend to backgrounds and physical appearance, prompting caution about their real-world accuracy.
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