Robert Lucian Chiriac’s article describes a DIY device using ML to predict license plates in real-time.
Earlier this year Rober Lucian Chiriacwrote about creating a DIY device that can recognize and read license plates. The plate reader utilizes a combination of on-device and cloud computing to work in real-time. The hardware for the project includes a Raspberry Pi, Pi camera, 4G antenna, and a GPS antenna. These items are housed in a DIY 3D printed case designed by @robertlchiriac.
Once the hardware was assembled, @robertlchiriac focused on training an ML pipeline to process video from the Pi camera into license plate predictions. This task was broken down into two main chunks, recognizing the license plate, and predicting the characters on the plate.
Yolo3 was chosen for the license plate recognition task and was fine-tuned on a small dataset of 534 images annotated by VOTT. The final model is available here and the dataset is available here.
keras-OCR was used to detect the characters on the license plates.
Once the hardware and the model were ready to go, Cortex was used to pull the two together to get the predictions in real-time. If you’re interested in seeing it in action, check it out on YouTube. If you’re interested in more projects like this checkout @robertlchiriac’s website.
Written by Rebecca Minich, Product Analyst, Data Science at Google. Opinions expressed are solely my own and do not express the views or opinions of my employer.
Adafruit publishes a wide range of writing and video content, including interviews and reporting on the maker market and the wider technology world. Our standards page is intended as a guide to best practices that Adafruit uses, as well as an outline of the ethical standards Adafruit aspires to. While Adafruit is not an independent journalistic institution, Adafruit strives to be a fair, informative, and positive voice within the community – check it out here: adafruit.com/editorialstandards
Stop breadboarding and soldering – start making immediately! Adafruit’s Circuit Playground is jam-packed with LEDs, sensors, buttons, alligator clip pads and more. Build projects with Circuit Playground in a few minutes with the drag-and-drop MakeCode programming site, learn computer science using the CS Discoveries class on code.org, jump into CircuitPython to learn Python and hardware together, TinyGO, or even use the Arduino IDE. Circuit Playground Express is the newest and best Circuit Playground board, with support for CircuitPython, MakeCode, and Arduino. It has a powerful processor, 10 NeoPixels, mini speaker, InfraRed receive and transmit, two buttons, a switch, 14 alligator clip pads, and lots of sensors: capacitive touch, IR proximity, temperature, light, motion and sound. A whole wide world of electronics and coding is waiting for you, and it fits in the palm of your hand.
Have an amazing project to share? The Electronics Show and Tell is every Wednesday at 7:30pm ET! To join, head over to YouTube and check out the show’s live chat and our Discord!
Python for Microcontrollers – Adafruit Daily — Python on Microcontrollers Newsletter: Open Hardware is In, New CircuitPython and Pi 5 16GB, and much more! #CircuitPython #Python #micropython @ThePSF @Raspberry_Pi
EYE on NPI – Adafruit Daily — EYE on NPI Maxim’s Himalaya uSLIC Step-Down Power Module #EyeOnNPI @maximintegrated @digikey
