Computer Vision-Driven Waste Classification for Automated Segregation Into Categories Like Recyclable, Organic, and Non-recyclable

This paper explores a computer vision-based approach to automatically sort waste into categories like organic, recyclable, and non-recyclable. Instead of relying on complex deep learning models, we’ve chosen to work with classic image processing techniques such as color analysis, edge detection, and segmentation. These methods allow the system to identify different types of waste based on their shape, color, and texture. The main aim is to speed up and simplify the waste sorting process—particularly useful in smart cities and industries embracing Industry 4.0. We tested our system using a labeled waste image dataset and found it to be both accurate and efficient. Our results suggest that even simple computer vision tools can play a valuable role in creating smarter, more sustainable waste management solutions. As per the observations the YOLOv5 is showing the best accuracy result with 94% and followed by ResNet50 with 93% F1-score.