Have you ever seen a deep learning based ANPR/ALPR (Automatic Number/License Plate Recognition) engine running at 47fps on ARM device (Android, Snapdragon 855, 720p video resolution)?
With an average frame rate as high as 47 fps on ARM devices (Snapdragon 855) this is the fastest ANPR/ALPR implementation you’ll find on the market. Being fast is important but being accurate is crucial. We use state of the art deep learning techniques to offer unmatched accuracy and precision. As a comparison this is #33 times faster than OpenALPR on Android. (see benchmark section for more information).
No need for special or dedicated GPUs, everything is running on CPU with SIMD ARM NEON optimizations, fixed-point math operations and multithreading. This opens the doors for the possibilities of running fully featured ITS (Intelligent Transportation System) solutions on a camera without soliciting a cloud. Being able to run all ITS applications on the device will significantly lower the cost to acquire, deploy and maintain such systems. Please check Device-based versus Cloud-based solution section for more information about how this would reduce the cost.
We’re already working to bring this frame rate at 64fps and add support for CMMDP (Color-Make-Model-Direction-Prediction) before march 2020. We’re confident that it’s possible to have a complete ITS (license plate recognition, CMMDP, bus lane enforcement, red light enforcement, speed detection, congestion detection, double white line crossing detection, incident detection…) system running above 40fps on ARM device.
On high-end NVIDIA GPUs like the Tesla V100 the frame rate is 315 fps which means 3.17 millisecond inference time. On low-end CPUs like the Raspberry Pi 4 the average frame rate is 12fps.
Don’t take our word for it, come check our implementation. No registration, license key or internet connection is required, just clone the code from Github and start coding/testing. Everything runs on the device, no data is leaving your computer. The code released on Github comes with many ready-to-use samples to help you get started easily.
You can also check our online cloud-based implementation (no registration required) to check out the accuracy and precision before starting to play with the SDK.
Github repo: https://github.com/DoubangoTelecom/ultimateALPR-SDK
Cloud-based implementation: https://www.doubango.org/webapps/alpr/
- C++ API
- Supported countries
- Architecture overview
- Device-based versus Cloud-based solution
- Configuration options
- Sample applications
- Getting started
- Parallel versus sequential processing
- Rectification layer
- Muti-threading design
- Memory management design
- Improving the accuracy
- Improving the speed
- Best JSON config
- Debugging the SDK
- Frequently Asked Questions (FAQ)
- Known issues