Deep Learning-Based Object Detection for Street Light Control with Energy Management and Fault Detection Using IoT

The proposed smart streetlight control system combines deep learning and IoT technology to improve energy efficiency, fault detection, and operational management. Utilizing sophisticated deep learning models, the system identifies human presence and other objects in real time, ensuring streetlights are activated only when needed. This targeted illumination approach reduces energy consumption while maintaining safety standards by lighting areas only during human activity or critical events. Additionally, the system employs various sensors to monitor streetlight performance, including parameters like light intensity, operational status, and fault conditions. Real-time data from the sensors is communicated to a central server through UART and processed by an IoT-enabled controller. Fault detection triggers instant alerts, schedules repairs, and updates relevant stakeholders, ensuring quick maintenance and uninterrupted operation. For remote management, the system integrates with the Blynk platform, providing real-time data visualization and control from any location. This feature helps municipal authorities monitor energy use, streamline maintenance processes, and maintain the reliability of streetlight infrastructure. By combining AI, IoT, and automation, this innovative solution offers a comprehensive framework for modern urban lighting. It significantly reduces energy wastage, lowers operational expenses, and enhances fault management, contributing to the development of sustainable and efficient urban environments. The scalable and cost-effective nature of this system supports the creation of smarter, greener cities.