Domain Name For Sale

Unlock the Potential of Your Premium Domain for Python in Deep Learning and Machine Learning!

Are you passionate about Python and its incredible applications in the world of deep learning and machine learning? Do you own a domain that...

Wednesday, June 14, 2023

Now Perform Real-time License Plate Detection and Recognition on CPU instead of GPU

ANPR/ALPR On CPU by Using this SDK

The UltimateALPR SDK provides various features for license plate recognition and related tasks, such as image enhancement, license plate country identification, vehicle color recognition, vehicle make model recognition, vehicle body style recognition, vehicle direction tracking, and vehicle speed estimation. These features are accelerated on different hardware platforms, including CPU, GPU, VPU, and FPGA, thanks to technologies like CUDA, NVIDIA TensorRT, and Intel OpenVINO.

The SDK achieves high performance on different hardware configurations. On high-end NVIDIA GPUs like the Tesla V100, the frame rate for license plate recognition is 315 frames per second (fps), resulting in an inference time of 3.17 milliseconds. On high-end CPUs like Intel Xeon, the maximum frame rate can reach up to 237 fps using Intel OpenVINO. Even on low-end CPUs like the Raspberry Pi 4, the average frame rate is 12 fps.

The SDK can be easily integrated into your projects, and no registration, license key, or internet connection is required. You can clone the code and start coding/testing on your local machine. The SDK provides ready-to-use code samples for Android, Raspberry Pi, Linux, and Windows, which can help you get started quickly.

Tesla Car ANPR implementation with alpr sdk
Doubango Telecom UltimateALPR SDK

For Android development, the SDK provides sample applications like Benchmark, VideoParallel, VideoSequential, and ImageSnap. These applications demonstrate the usage of the SDK and can be run on Android devices. The SDK can be added to your Android project by including the source directly or by referencing the Android Studio module.

For Raspberry Pi, Linux, NVIDIA Jetson, Windows, and other platforms, the SDK provides C++ sample applications like Benchmark and Recognizer. These samples can be used across different supported platforms and demonstrate license plate recognition from images. The SDK also provides API documentation for the C++ API, which defines the functions and parameters required for integration.

The SDK utilizes hardware acceleration capabilities to achieve high performance on CPUs. It leverages technologies like CUDA, NVIDIA TensorRT, and Intel OpenVINO to optimize the execution of license plate recognition tasks. By utilizing parallel processing and optimized algorithms, the SDK can efficiently process video frames and deliver accurate results in real-time.

In summary, the UltimateALPR SDK offers a comprehensive solution for license plate recognition and related tasks. It provides support for different hardware platforms, offers high performance through hardware acceleration, and provides easy integration options for various programming languages.

While it is true that some complex deep learning models, such as YOLO (You Only Look Once), may not perform optimally on CPUs compared to GPUs, there are several factors that allow the UltimateALPR SDK to work efficiently on CPUs:

Optimization: The UltimateALPR SDK is specifically designed and optimized for efficient execution on CPUs. The algorithms and models used in the SDK are carefully optimized to leverage the available CPU resources and deliver accurate and fast license plate recognition results.

Hardware-specific optimizations: The SDK takes advantage of specific optimizations and libraries that are optimized for CPU architectures. For example, the SDK can utilize Intel OpenVINO to optimize the execution of deep learning models on Intel CPUs. These optimizations improve the performance of the SDK on CPUs, allowing it to achieve faster inference times.

Task-specific requirements: License plate recognition is a specific task that does not necessarily require the same level of complexity and computational resources as other tasks like object detection or image segmentation. License plate recognition primarily involves character recognition and pattern matching, which can be efficiently performed on CPUs.

Architecture and Efficiency

The deep learning models used in the UltimateALPR SDK are designed to strike a balance between accuracy and computational efficiency. These models are carefully crafted to deliver accurate license plate recognition results while being optimized for CPU execution.

It's important to note that the performance of deep learning models can vary depending on the specific model architecture, the complexity of the task, and the hardware resources available. While some models like YOLO may not perform well on CPUs, other models and algorithms, like the ones employed in the UltimateALPR SDK, can be designed and optimized to work efficiently on CPUs.

Additionally, it's worth mentioning that the UltimateALPR SDK also provides hardware acceleration options, such as GPU and VPU support, for users who have access to these resources and want to achieve even higher performance. The SDK offers flexibility in utilizing different hardware resources based on the specific requirements and available options of the user's system.

Python Binding and Wrapper for Developers

A Python wrapper is a piece of code that allows you to use a library or API written in another programming language (such as C++ or C) within Python. It acts as an interface or bridge between the Python programming language and the underlying code written in another language.

In the context of the UltimateALPR SDK, a Python wrapper refers to a module or package that provides a convenient way to use the SDK's functionality and features from within Python. It wraps the original C++ or C code of the SDK and exposes it as Python functions, classes, or objects, making it easier for Python developers to integrate and work with the SDK in their Python projects.

The Python wrapper for the UltimateALPR SDK typically includes functions or classes that allow you to initialize the SDK, process images or video frames, access recognition results, configure parameters, and perform other operations related to license plate recognition. It provides a Pythonic interface to interact with the underlying SDK functionalities.

Using the Python wrapper, developers can leverage the power of the UltimateALPR SDK and its license plate recognition capabilities directly in their Python applications, without needing to write low-level code or understand the intricacies of the underlying C++ or C implementation.

The Python wrapper enhances the usability and accessibility of the UltimateALPR SDK for Python developers, enabling them to leverage the SDK's features while staying within their preferred programming environment.

In summary, to achieve automated plate recognition results similar to the video demonstration, You can write your own code using OpenCV for process video streams. They can test the results using Doubango's recognizer.exe or the online ALPR demo, and for a more thorough evaluation, they should compare accuracy scores using a sizable dataset.

No comments:

Post a Comment