IoT for Smart Parking System

Current smart parking systems primarily use technologies like ultrasonic sensors, RFID, and image recognition to detect parking space occupancy and assist drivers in finding available spots. These methods often face challenges in terms of accuracy, especially in dynamic environments with fluctuating lighting conditions, vehicle types, or obstructions. The proposed system introduces significant improvements by incorporating YOLOv8, an advanced deep learning-based object detection model, to enhance the precision and speed of vehicle detection. By utilizing real-time video feeds and robust data processing, this system can track vehicles more accurately, even in complex environments. Moreover, it offers real-time updates via an HTTP protocol, directly transmitting data to a web-based interface, ensuring seamless user interaction for parking guidance and space availability monitoring. This approach not only reduces time spent searching for parking but also enhances the overall user experience and operational efficiency compared to traditional systems.