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Polythene has always been a threat to the environment since its invention. Itis non-biodegradable and very difficult to recycle. Even after many awarenesscampaigns and practices, Separation of polythene bags from waste has been achallenge for human civilization. The primary method of segregation deployed ismanual handpicking, which causes a dangerous health hazards to the workers andis also highly inefficient due to human errors. In this paper I have designedand researched on image-based classification of polythene bags using adeep-learning model and its efficiency. This paper focuses on the architectureand statistical analysis of its performance on the data set as well as problemsexperienced in the classification. It also suggests a modified loss function tospecifically detect polythene irrespective of its individual features. It aimsto help the current environment protection endeavours and save countless liveslost to the hazards caused by current methods.

Polyth-Net: Classification of Polythene Bags for Garbage Segregation Using Deep Learning