This week I’m mainly working on the cv part. I trained model for individual type of chess pieces, and tested them like this:
I’ve also started to integrated all the photos I took for each type of chess pieces into one model. I will need to take more photos with multiple pieces in one frame to train the model so that it will learn to tell the pieces apart. So thats my plan for next week, and also write code for the rest of the cv part such as taking a frame in live videosteam when nothing moves in 200ms, and board capture with aruco markers. Then I should be able to get output from my subsystem that can be fed into the led system and chess engine for integrating with my teammates.
I am on schedule and no changes will be made to the schedule.
About verification on my subsystem, the physical board won’t need any verification, so it’s mainly about the cv subsystem. I will evaluate detection performance of the yolox model both on precision and recall, the overall accuracy needs to exceed 95% to meet our expectations. The evaluation will be done with datasets including different circumstances such as dark environment, or different angels of camera placement. I will also measure the end-to-end detection latency from image capture to output to make sure it’s under 1s to minimize the users’ notice on latency.
the phone mount set up looks like this:
