论文标题
polyth-net：使用深度学习的聚乙烯袋分类用于垃圾隔离
Polyth-Net: Classification of Polythene Bags for Garbage Segregation Using Deep Learning
论文作者
论文摘要
自发明以来，聚乙烯一直是对环境的威胁。它是不可生物降解的，很难回收。即使经过许多意识运动和实践，聚乙烯袋与废物的分离也是人类文明的挑战。部署隔离的主要方法是手动挑选，这会对工人造成危险的健康危害，并且由于人为错误而效率也很低。在本文中，我使用深度学习模型及其效率设计并研究了基于图像的聚乙烯袋的分类。本文着重于对数据集的性能以及分类中遇到的问题的架构和统计分析。这也建议修改后的损耗函数，以特异性检测聚乙烯，而与其单个特征无关。它旨在帮助当前的环境保护努力，并挽救无数生命，损失因当前方法造成的危害。
Polythene has always been a threat to the environment since its invention. It is non-biodegradable and very difficult to recycle. Even after many awareness campaigns and practices, Separation of polythene bags from waste has been a challenge for human civilization. The primary method of segregation deployed is manual handpicking, which causes a dangerous health hazards to the workers and is also highly inefficient due to human errors. In this paper I have designed and researched on image-based classification of polythene bags using a deep-learning model and its efficiency. This paper focuses on the architecture and statistical analysis of its performance on the data set as well as problems experienced in the classification. It also suggests a modified loss function to specifically detect polythene irrespective of its individual features. It aims to help the current environment protection endeavours and save countless lives lost to the hazards caused by current methods.
