A solar-powered vehicle counter built on Raspberry Pi 5 uses YOLOv8 machine vision and a USB webcam to detect and count vehicles by direction in real time, with periodic uploads to a cloud MySQL database. The system employs Kalman filtering and IoU matching to track individual vehicles across frames and classify their direction of travel without interfering with road infrastructure.
A year-long project trained traffic-sign detectors using only synthetic rendered images, achieving 75–84% of the performance of models trained on real photographs. Fine-tuning the synthetic-pretrained model on real data improved detection by 0.15 mAP50, with the largest gains on underrepresented sign classes, though modeling snow proved ineffective due to lack of real snowy conditions in test data.