论文标题
基于学习的塑料废物隔离的分类
One-Shot learning based classification for segregation of plastic waste
论文作者
论文摘要
对于许多国家来说，隔离可回收废物的问题相当令人生畏。本文介绍了一种使用单光学习技术对塑料废物进行基于图像的分类的方法。所提出的方法利用了通过暹罗和三重态损失卷积神经网络产生的歧视性特征，以根据其树脂代码帮助区分5种塑料废物。该方法在Wadaba数据库中获得了99.74％的精度
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
