License plate parsing using Darknet and YOLO



DarkPlate Demo

Note that DarkPlate by itself is not a complete software project. The intended purpose was to create a simple project showing how to use Darknet/YOLO, DarkHelp, and OpenCV to find license plates, parse them, and display the results.


The 3 dependencies are:

  • OpenCV (image processing library)
  • Darknet (neural network framework for YOLO)
  • DarkHelp (C++ API wrapper for Darknet)

Installing these is explained on the DarkHelp repo:

Build and run

Once the dependencies have been installed, run the following commands to build and run DarkPlate on Ubuntu:

mkdir -p ~/src
cd ~/src
git clone
cd DarkPlate
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
src/DarkPlate ../media/m1.mp4
  • Multiple video source

    Multiple video source

    Good day! Thank you very much for the tool, it is very useful and necessary! How can I run multiple video sources?

    Or at least how can you run the code with the rtsp camera?

    P.S Please make a donation button on the channel so that we can support you! Thanks again)

    opened by MichaelBryce90 7
  • Make error

    Make error

    Hi Stephan,

    Love the youtube videos and the projects you have created. I am new and working on a hobby project of mine. I installed all the pre requirements of DarkPlate but when I run sudo make I get the following: [email protected]:~/src/darkplate/build$ sudo make Consolidate compiler generated dependencies of target DarkPlate [ 50%] Building CXX object src/CMakeFiles/DarkPlate.dir/main.cpp.o /home/cosme/src/darkplate/src/main.cpp: In function ‘void process_plate(DarkHelp::NN&, cv::Mat&, cv::Mat&)’: /home/cosme/src/darkplate/src/main.cpp:77:25: error: loop variable ‘prediction’ creates a copy from type ‘const DarkHelp::PredictionResult’ [-Werror=range-loop-construct] 77 | for (const auto prediction : results) | ^~~~~~~~~~ /home/cosme/src/darkplate/src/main.cpp:77:25: note: use reference type to prevent copying 77 | for (const auto prediction : results) | ^~~~~~~~~~ | & cc1plus: all warnings being treated as errors make[2]: *** [src/CMakeFiles/DarkPlate.dir/build.make:76: src/CMakeFiles/DarkPlate.dir/main.cpp.o] Error 1 make[1]: *** [CMakeFiles/Makefile2:98: src/CMakeFiles/DarkPlate.dir/all] Error 2 make: *** [Makefile:91: all] Error 2

    Any ideas? My guess is user error :)

    Thanks, Cosme

    opened by hurlyburlymarley 6
  • Awesome work

    Awesome work

    Hi Stephane,

    This is really good, just one issue. How can I reduce the input video size to make it run smoothly. Right now its running the input video in full 1080p mode, hence slow. Is there any way to pass the video in lower resolution to the inference model so as to make it realtime ?

    opened by rsingh2083 2
  • about license plate datasets

    about license plate datasets

    Dear Stephan: I think you used the yolov4-tiny model for training. I want to use yolov4 model to train the license plate detection model. Would you please provide your labeled dataset? Thanks a lot!

    opened by opentld 1
  • How to add class names in DarkPlate

    How to add class names in DarkPlate

    Hi Stephan,

    I copied my images directory in DarkPlate/nn directory , but while loading the images I can see only 4 classes :-


    There is no "plate" or number/alphabet class at all. How to add these classes ?

    opened by rsingh2083 1
  • How to train with gray channel only?

    How to train with gray channel only?

    Hello, Stephane! Could you tell me how to train a model only on a gray channel? I do not need RGB, as many experiments have shown that special modes of compensation of light from the camera in the black and white image mode work much better than the color image. In this regard, it is necessary to test the model with only the gray channel. What do I need to change to get a single channel model? Thanks in advance!

    opened by MichaelBryce90 1
  • More details

    More details

    Could you explain better the idea behind this projected? As far I am concerned, it looks like you :

    1. Trained YOLO to recognize (Letters/Numbers + License Plate)
    2. Parse video, detect each frame
    3. Filter only detections inside 'License Plate detection bbox'
    4. Read detections labels from left to right
    opened by folkien 2
Stéphane Charette
C/C++ developer. Mostly linux-based. IoT, desktop, and embedded device. Computer vision, neural networks, Ubuntu geek.
Stéphane Charette
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