AI-BASED GATEMAN SYSTEM To Record Vehicle Number Plates

An earlier version of this automatic gateman system, built around a camera-based design, was published on the Electronics For You website and can be accessed here:

- SOMNATH BERA

https://www.electronicsforu.com/electronics-projects/automatic-gateman

That system used an ultrasonic distance sensor to detect approaching vehicles, after which a Raspberry Pi camera captured the number plate image. While the concept worked, its performance revealed several practical limitations. The ultrasonic sensor could not reliably distinguish between a vehicle and a person, so anyone carrying a fake number plate could unintentionally trigger the capture process. Recognition accuracy was inconsistent, and overall operation remained slow. To address these shortcomings, a more advanced and robust architecture has been developed. In the updated system, the camera not only captures and stores the vehicle's number plate in a database but also runs an additional machine-learning model in parallel to classify the approaching object.

This enables clear differentiation between vehicles, humans, animals (such as cats and dogs), fake boards, and counterfeit number plates. The system first confirms that the detected object is a vehicle and only then proceeds to locate and recognise the number plate.

A YOLO-based object-detection framework supports this process. Two models, YOLO and kbest.pt, operate simultaneously to detect the vehicle and subsequently extract and recognise its number plate with higher accuracy.

Together, these enhancements improve robustness, reliability, and operational efficiency. The system runs on a Raspberry Pi 5 (or Raspberry Pi 4), selected for its strong processing capability and suitability for edge-based AI applications. Continuous YOLOv8 inference generates considerable heat, making effective thermal management essential.