SMART STREET – GARBAGE DETECTION & ALERT SYSTEM

Abstract

The rapid growth of urban populations has significantly increased waste generation, making efficient garbage management a critical challenge for smart cities. This project proposes a Smart Street Garbage Detection and Alert System that leverages computer vision and machine learning techniques to automatically detect garbage accumulation in public areas. The system utilizes image processing models, such as convolutional neural networks (CNNs), to identify waste in real-time through surveillance cameras or mobile inputs. Upon detection of garbage, the system generates automated alerts to municipal authorities, enabling timely waste collection and improved sanitation. Additionally, the system maintains a centralized database for monitoring garbage levels, tracking locations, and analyzing patterns for better decisionmaking. This approach reduces manual inspection, enhances operational efficiency, and promotes cleaner urban environments. The proposed solution contributes to the development of smart cities by integrating artificial intelligence with waste management systems, ensuring sustainability, public hygiene, and environmental protection.