I’m trying to count usb sticks on a conveyor belt and try to check how they are oriented (left /right) in real time.
And there is the problem, the belt moves quite quickly, so I think I need 20+ FPS.
I had two approaches:
- Since I know where the sticks come on the belt, I had a fixed window, which i forwarded to an image classifier (Mobilnet V2 96x96). This gave me the orientation with accuracy and then I used optical flow to track them and count them. Output due to slow optical flow around 17 fps.
- I trained a SSD Mobilnet v2 and gave it a bigger picture of the belt which gave me orientation and when bboxes crossed a line i counted one up. gave me ±15 FPS depending on how many parts were in the picture.
both worked really well with video, but I would love to have it in real time :)
So I though maybe some of you had similar projects and can give me a little hint ;)
Thanks a lot
P.S. I am using tensorflow and tried to optimize my models with TensorRT, but didn’t see any improvement…