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Plastic Detection in The Surrounding Using Machine Learning Technique

Authors:

Navya V, Pallavi R H, Prathibha R, Bhoomika V N

Abstract—Plastic pollution has become an important envi- ronmental threat that impacts ecosystems, marine life, and global human health. The growing volume of plastic waste necessitates more efficient detection and management methods, beyond traditional manual approaches. This paper introduces a machine learning-based system to detect plastic waste in various environments, providing an automated solution to support waste management practices. The system employs advanced image processing techniques alongside Convolutional Neural Networks (CNNs) and the YOLOv5 object detection model to identify plastic materials in real time. Despite showing high accuracy and adaptability, the system offers a scalable tool for detecting plastic waste in various settings. The applications of the system include the deployment on mobile platforms, drones, and integration into waste management systems, contributing to ecological sustain- ability and improving recycling processes. Future improvements will focus on expanding the dataset, improving model precision, and incorporating IOT technologies for broader applications.

Index Terms—Keywords: Plastic detection, machine learning, image classification, YOLOv5, environmental monitoring, waste management, sustainability.