I finished creating the detection algorithm. Instead of using the yOLOv4, I used the R-CNN instead. R-CNN typically provides better accuracy by employing region-based convolutional neural networks, which allow for more precise localization of objects in images, albeit at the cost of increased computational complexity during inference. The bounding boxes I was able to create were highly accurate but took a long time to be produced. Ran tests for the detection using static images of the slot car from various angles and distances. Then I integrated the pre-processing, detection, and tracking. Ran tests on a video of the slot car. Currently, the detection algorithm might be too slow, and the preprocessing needs to be tuned after testing which configuration works best for latency. I also have actual footage of the toy car on the track that I will be testing my algorithm on now.
For next week, I will be integrating the code with the live stream offered by our cameras and relaying panning instructions to the motors. Further testing on what type of tracker/detection will be best for our use case will be required.
Given the amount of fine-tuning our system will require for the live stream to be watchable I think we are slightly behind schedule. Sufficient testing in the following week should help put us back on track