Last week, we conducted a live test of the walk sign image classifier using the initial model. This involved running the model on the Jetson and feeding it images captured by the camera once every 2 seconds. By also saving the image that was being fed into the model, I could then later view the image on my computer and see exactly what the model was seeing. The performance during the live test was not great and the model frequently predicted “GO” and only rarely predicted “RED”. To try and fix this, we collected 400 more images using the same camera and retrained the model. I re-evaluated the performance with a test dataset and it seems to be better now, but I still need to conduct more field tests and then likely repeat this process if it’s not performing well enough. I also need to work on implementing the rest of the control logic for switching from the walk sign classification to the object detection process.
