The Little Computer that Could: YOLO Object Recognition and Raspberry Pi Part 2 of 3

 

You Only Look Once

One cure for motion detector false alarms in Part 1 is object recognition; the alarm is only raised if the intruder is recognized. YOLO is fast, trendy (deep neural net!) and will run on a Raspberry Pi. arunponnusamy comes straight to the point.

I already had numpy and openCV from Part 1, so it is just

# wget https://pjreddie.com/media/files/yolov3.weights

Now this is a massive file and I needed a copper Ethernet connection to the Internet to download it. Next you download the zip file from arunponnusamy for the files yolo_opencv.py, yolov3.cfg, yolov3.txt and dog.jpg. Do not use wget on github. And simply run it:

# python3 yolo.py --image dog.jpg --config yolov3.cfg --weights yolov3.weights --classes yolov3.txt

If you get 'OutOfMemoryError' you will probably need to reboot your Pi.

The output should be:

I substituted a frame from my IP Camera, and sure enough it recognized a person correctly:

# python3 yolo.py --image alarm.png --config yolov3.cfg --weights yolov3.weights --classes yolov3.txt

Output of yolov3.weights model

For convenience, see my github repository for copies of arunponnusamy's code. It took my Raspberry Pi 3 some 50 seconds to process the image, which is a little long but would probably keep up with the motion detector triggers of a few frames per hour. But arunponnusamy's reference is Joseph Redmon, who also mentioned a tiny version of the pre-trained neural net model. You will also need to download his yolov3-tiny.cfg, which I got by cloning from his github.

git clone https://github.com/pjreddie/darknet

Joseph's executable is a C program, but the input files are the same so I simply used his tiny model files with arunponnusamy's python script:

$ python3 yolo.py --image alarm.png --config yolov3-tiny.cfg --weights yolov3-tiny.weights --classes yolov3.txt

Note I also reused arunponnusamy's yolov3.txt which is the dictionary of object names. Joseph's dictionary seems to be baked into his executable file. The command finishes in 5 seconds, which is 10 times better. The output bounding box is different, but it did recognize the object as a person.

Output of yolov3-tiny.weights model

The idea is to use YOLO to filter out the false alarms from the motion detection program from Part 1, In Part 3, we will look at tiny-YOLO, Tensorflow, and SSD_Lite. The Raspberry Pi is truly the little computer that could.

Happy Trails.