Abstract
This project uses the Ultra96 FPGA board to detect real-time traffic signals and recognizeobjects with the help of hardware acceleration and Python-based algorithms. It showcases
the FPGA's processing ability by handling inputs from two USB cameras. A Logitech USB camera and a monochrome USB camera. At the same time. The implementation
incorporates sophisticated detection algorithms such as TensorFlow Lite, YOLO, and Haar Cascade, resulting in speed and accuracy [39]. The system for detecting is based on a
TensorFlow Lite model that has been trained and optimized for edge devices using methods like quantization. The training data consisted of 1,000 images of traffic lights
taken under various lighting and weather conditions. To improve the model's robustness, data augmentation techniques like rotation, scale modifications, and brightness alterations
were used.
The system identifies traffic signals as objects. Understands their color (red or green),
allowing for accurate on-the-spot detection. This project highlights the advantages of
FPGA hardware acceleration over processors such as Raspberry Pi. The Ultra96 FPGA
demonstrates superiority in speed and efficiency while offering scalability suitable for
utilization in intelligent traffic control systems, self-driving vehicles such as autonomous
cars, and roadway safety surveillance. System deployment is performed via Jupyter
Notebook to provide an adaptable development workflow. The images capture the system's
strengths well. The Logitech camera succeeds in displaying resolution and correctly
detecting colors, whereas the monochrome camera does well in low-light conditions. This
clever blend of FPGA acceleration with a software framework and real-time object
detection provides a flexible and feasible answer for advanced traffic control systems.