Plastic Waste Identification Using Deep Learning for Adequate Waste Management

Abstract

Plastic waste management has emerged as a critical global challenge, prompting concerted efforts from conservation authorities and international organizations like the United Nations to enhance detection and classification strategies. This research distinguishes itself by harnessing advanced deep learning techniques to identify plastic materials at the micron level, surpassing traditional macro-level methods. The primary goal is to classify plastics into four major distinctions, addressing a key challenge in plastic segregation by accurately measuring thickness. Leveraging the YOLOv8 architecture, this approach enables precise classification of plastics into Polyethylene terephthalate (PETE), High Density Polyethylene (HDPE), low density polyethylene (LDPE0, and Polyvinyl Chloride (PVC) categories based on thickness. To achieve this, incorporation of hardware components such as ultrasonic sensors and NodeMCU for detecting thickness variations is used. By facilitating effective segregation according to environmental impact, this innovation revolutionizes waste reduction efforts, offering real-time identification and enhancing overall sustainability in plastic waste management.