Stop Sign Detection

This part utilize a Google Coral accelerator and a pre-trained object detection model by Coral project to perform stop sign detection. If the donkey car see a stop sign, it will override the pilot/throttle to 0. In addition, a bounding box will be annotated to the cam/image_array.

Requirement

To use this part, you must have:

Google Coral USB Accelerator

How to use

Put the following lines in myconfig.py

STOP_SIGN_DETECTOR = True
STOP_SIGN_MIN_SCORE = 0.2
STOP_SIGN_SHOW_BOUNDING_BOX = True

Install Edge TPU dependencies

Follow the Coral Edge TPU get started instructions to install the necessary software. For the RaspberryPi follow the Linux instructions.

The stop sign detector uses a pre-compiled model, so we only need the inference runtime to make this work. However, if you are creating your own model then you will need the Edge TPU Compiler on your RaspberryPi (or Linux laptop if you are training on that). Note that the compiler only runs on Linux.

Detecting other objects

Since the pre-trained model are trained on coco, there are 80 objects that the model is able to detect. You can simply change the STOP_SIGN_CLASS_ID in stop_sign_detector.py to try.

Accuracy

Since SSD is not good at detecting small objects, the accuracy of detecting the stop sign from far away may not be good. There are some ways that we can make enhancement but this is out of the scope of this part.

Getting this to work without the Coral Edge TPU

There is an issue in the Github for making this work without the Coral Edge TPU. If you get this working please submit a pull request.