Accomplishments
This week the Oak-D Pro camera arrived, so I was able to get the camera set up and running with the DepthAI Python API, and launched a few examples to test out the capabilities of the camera. Since the example object detection models werenot very specific to custom objects, I trained my own YOLOv8 detection model on ping pong ball examples based on the ImageNet dataset. I chose YOLOv8 as it supported training on Mac M3 Silicon using MPS (Metal Performance Shaders) for GPU acceleration. This was already good enough to be able to detect a white ping pong ball that we had, however the bounding boxes would have artifacts and would not be able to accurately detect fast movements.
Schedule
Based on last week’s deliverables, I am very pleased with my progress and am on track with my schedule as I have trained the initial detection model, although there is still much work to be done in the detection and estimation areas.
DeliverablesÂ
Next week, more work would need to be done on converting and compiling my trained model onto the Oak-D camera, as it takes a specific MyriadX Blob format which is suitable for the onboard camera model acceleration processor. The performance of the model will also be an issue, and more testing will need to be done I will also aim to take the bounding box information and extract the depth information from the depth sensor. Another project part that I will start to spearhead is working on a Kalman filter estimation model for the ball trajectory.