Smart Waste Segregation System: Proximity-Triggered Waste Classification Using Convolutional Neural Networks

Abstract

The rapid increase in the amount of waste produced by human societies has led to the urgent need for proper waste management so as to curtail risks to both health and the environment. The infrastructure facilities and public awareness are a prerequisite for effective automation, still are often neglected. This paper introduces a Smart Waste Segregation System (SWS) aimed at increasing efficiency in managing waste by automating its classification and disposal activities. The Arduino microcontroller is used in this system along with an OV7670 camera and an ultrasonic sensor for accurate contactless classification and disposal of waste into different categories. When waste is placed on a designated flap, the image is captured by an OV7670 camera. The image is subsequently analysed by a deep learning model trained on a diverse dataset containing images of various category waste items. Based on the model’s classification, the waste can be disposed in the appropriate bin.