This week, I created a quantized YOLO model and tuned it in order to increase speed of the model on the Pi. I also rewrote an integration script, making optimizations with the OCR intervals and threading in order to reduce the inference time on each frame when running the end-to-end integration script on the Pi.
For unit tests, I ran the video processing script on a set of manually recorded videos (using printed signs) as well as pre-existing image datasets (117 airport signs) and reached an accuracy of approximately 92% with <100ms of preprocessing and inference time on the Mac. Eventually, the new script yielded a latency of <2s on the Raspberry Pi, which met our initial use case requirements.