CNN Based Smart Waste Segregation and Collection System

Waste management is an increasing concern in urban areas worldwide, because of population growth and environmental sustainability goals. To address this challenge, this paper presents a Convolutional Neural Network (CNN)-based waste segregation and collection system that uses the power of computer vision and machine learning. The proposed system is used to automate the process of waste segregation, optimizing the sorting of waste into bio-degradable, reusable, recyclable waste and trash in real time. Utilizing a camera for capturing the images of incoming waste materials and moving the waste at their respective collection points. The images are processed using a deep learning YOLOv8 model, which identifies and classifies the waste items into bio-degradable, recyclable and non-recyclable categories with high accuracy. This innovative waste management solution holds the potential to change the waste collection and recycling processes, making them more cost-effective, environmentally friendly, and aligned with the ability for sustainable waste management practices.