I am working on a robotics project that requires realtime object detection and tracking. The code reaches about 8fps on raspberry pi 4 B and I need it closer to 30fps.
I got a coral usb tpu accelerator and downloaded the tflite examples for object detection that have a model for the coral TPU: https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/raspberry_pi
There are two options for this demo:
python3 detect.py --enableEdgeTPU --model efficientdet_lite0_edgetpu.tflite
Running the object detection with either shows the same FPS ~8fps. The coral accellerator does blink when I run detection with TPU mode but I see no performance gains.
Originally I thought this could be a power consumption issue so I plugged it into an externally powered usb hub, that didn’t help. Then I though maybe it was a hardware issue so I got another coral accellerator and unfortunately got the same results.
Hoping someone can point me in the right direction here or offer another approach, I have been banging my head against the wall trying to improve performance in a bunch of ways and it feels like the TPU accelerator is the only thing that is going to get me there on edge hardware.