AI-Based Camera Systems for Roadside Litter Detection and Offender Identification

Urban littering continues to be a persistent environmental and civic challenge, adversely affecting public health, sanitation systems, and urban aesthetics. Traditional litter monitoring mechanisms largely rely on manual surveillance, post-incident cleaning, and punitive enforcement, which are inefficient,resource-intensive and limited in scalability.In recent years, advances in artificial intelligence, particularly in computer vision and deep learning, have enabled the development of automated systems for real-time litter detection and monitoring.The proposed system integrates YOLOv8-based object detection, face detection, and OCR-based license plate recognition to identify littering events and associated offenders in real time. A Streamlitbased dashboard enables live monitoring and incident management, while a Telegram-based alert mechanism provides timely notifications with visual evidence. Experimental observations demonstrate the practical feasibility of the system for real-world roadside surveillance. The framework emphasizes deployment feasibility, scalability, and ethical considerations for smart city applications.