It is hardly an original desire to want to know who or what is coming up the driveway. As a step along that road (as it were), I used my YOLO workflow to train YOLOv3 on a few things likely to be seen there. With my usual impatience, this test captured above was performed with an early set of weights (at around 1200 iterations) but actually seemed to work reasonably well and was easily able to differentiate between the different vehicle types and makes. Training is continuing again now but it is nice to know that it is going to work. I am training it to detect a range of vehicles, including UPS trucks and mail vans.
One thing I don’t know as yet is the situation with false positives – will random cars and trucks trigger one of the learned classes or not? Time will tell. If so, I’ll probably have to include some negative examples in the training set that includes examples of other types of vehicles that I don’t want to detect. Or, put all these other examples into a new general vehicle class. Not sure which is best at this point.
This is the fairly boring rt-ai Edge design that’s using the new model. It is basically passing the video frames through CYOLO and then pushing out to Manifold where it is being stored and can be viewed in real-time. This is running full time now so I will be able to look back and see how the detection performs in real life. In addition, selected and annotated frames from the stored data can be recycled to add to the training data in a future training cycle.
I could go crazy and use the license reading SPE to be much more specific about the individual vehicles. However, I still don’t have the right sort of cameras to make that work effectively.
Ok, so now that I have YOLO producing metadata indicating what is moving on the driveway, I then need to process that into useful information. That’s going to require a new SPE to process and filter the raw detections so that I can get real-time alerts for interesting events.