SMART LITTER DETECTION IN MARKET STREETS USING AI-BASED MONITORING SYSTEMS

Abstract

The rapid growth of urban populations and commercial activities in market streets has led to increasing challenges in maintaining cleanliness and effective waste management. Traditional monitoring methods rely heavily on manual inspection, which is time-consuming, inefficient, and often inconsistent. To address these issues, this paper proposes a Smart Litter Detection in Market Streets Using AI-Based Monitoring Systems, which leverages artificial intelligence and computer vision techniques to automatically identify and monitor litter in public spaces. The proposed system utilizes surveillance cameras integrated with deep learning models, such as convolutional neural networks (CNNs), to detect various types of waste materials including plastics, paper, and organic debris in real-time. The system processes video feeds to identify litter presence, location, and accumulation patterns, enabling timely intervention by municipal authorities. Additionally, the platform can generate alerts and visual reports, improving response efficiency and resource allocation for cleaning operations. The model is trained on diverse datasets representing different environmental conditions, ensuring robustness against variations in lighting, weather, and crowd density. Experimental results demonstrate high detection accuracy and real-time performance, making the system suitable for deployment in busy market environments. Furthermore, the solution supports smart city initiatives by promoting automation, sustainability, and improved urban hygiene. Overall, the proposed AI-based monitoring system provides an efficient, scalable, and cost-effective approach to litter detection and management, contributing to cleaner and healthier urban environments.