AI-Based Smart Waste Segregation Using YOLOv11 and MobileNetV2

Improper waste segregation is a major challenge in effective waste management. Manual segregation is timeconsuming, labor-intensive, and may expose workers to unsafe materials. This paper presents an artificial intelligence based smart waste segregation system using computer vision and deep learning. The proposed system uses YOLOv11 for real-time object detection and MobileNetV2 for waste classification. The system is designed to identify waste items from camera images and classify them into predefined categories such as wet, dry, and metal waste. A web-based dashboard is also developed to display the live camera feed, detected object, confidence score, and waste statistics. The current implementation focuses on the software and computer vision pipeline without requiring physical IoT segregation hardware. The proposed approach provides a foundation for future integration with sensors, microcontrollers, servo motors, and automated waste bins.