How does License Plate Recognition work?
A new technology that has been making headlines in the past few years is called License Plate Recognition (LPR). It is a system that can read license plates and create a data set. The system can then be used by law enforcement to identify information about a vehicle to make catching criminals easier. The system can read plates from up to 700 feet away and can read more than 50 plates per second. Optical character recognition (OCR) using deep neural networks is a popular technique to recognise characters in any language.
ANPR cameras are claimed to have an advantage over traditional speed cameras in maintaining steady legal speeds over extended distances rather than encouraging heavy braking on the approach to specific camera locations and subsequent acceleration back to illegal speeds. The first experimental system was tested on a short stretch of the A2 in the UK in 1997 and was deemed a big success by the police. More recently, cloud technology means a rise in the popularity of license plate recognition online.
Try our license plate recognition app. Capture ANPR. It's free on the App store.
ANPR in Australia
ANPR has been used on toll roads for over 20 years. It’s only been in the past 3 years that AI has made the technology more accurate and obtainable for smaller budgets.
How is License Plate Recognition Helping Us?
License plate recognition, or LPR, is a technology helping us in several ways. For one, it’s helping law enforcement agencies solve crimes and track down wanted criminals. It’s also helping businesses and private individuals keep track of their vehicles. Additionally, LPR is being used to help manage parking lots and garages and even to help with toll collection.
Security police forces are already using License Plate Recognition for security and law enforcement through embedded cameras on their vehicles, on Segways or fixed control points to detect stolen, wanted or uninsured vehicles
Some everyday use cases include parking assistance systems, automated toll booths, vehicle registration and identification for delivery and logistics at ports, and medical supply transporting warehouses. Free Flow / Ticketless Parking Touchless, frictionless & ticketless parking installations where parkers don't have to stop at barriers parkers to enter, pay or exit: a smoother, faster and cleaner parking experience with minimum investment, thanks to ANPR.
In what countries is ANPR popular?
ANPR is popular in many countries worldwide, including the United States, Canada, the United Kingdom, South Africa and Australia.
License Plate Recognition Phases
The first phase in license plate recognition is to detect the position of the license plate in the image. This can be done using various image processing techniques like edge detection, colour detection, etc. Once the position of the license plate is detected, the next phase is to segment the license plate region from the rest of the image. This is usually done by using a combination of colour and edge information. After the license plate region is segmented, the next phase is to extract the characters from the license plate. Using various image processing techniques like connected component analysis, character recognition, etc.
What are the basics of ANPR machine learning?
ANPR is the process of using machine learning to read license plate numbers from images automatically. This can be done using a variety of methods. Still, the most common is to use a convolutional neural network (CNN). CNNs are a type of artificial neural network that is particularly well suited for image recognition tasks.
To train a CNN to read license plate numbers, you first need a dataset of images that contain license plate numbers. This dataset can be collected from various sources, such as security cameras or open-source pictures from the internet. Once you have this dataset, you can then begin the process of training your CNN. This involves feeding the CNN images from the dataset and telling the CNN what the corresponding license plate numbers are. The CNN will then learn to recognise the patterns in the photos that represent license plate numbers.
Once your CNN is trained, it can then be used to read license plate numbers from images automatically. This process is typically much faster and more accurate than trying to read license plate numbers manually. Additionally, it can be used to read license plate numbers from images that are taken from a variety of angles, lighting conditions, and distances.
One of the benefits of using a CNN for license plate recognition is that the CNN can be trained to read various types of license plate numbers. For example, a CNN can be trained to read both European-style license plate numbers and North American-style license plate numbers. Additionally, a CNN can be trained to read license plate numbers from various countries. This is important because license plate numbers can vary significantly from one country to another.
There are a few things that you need to keep in mind when training a CNN for license plate recognition. First, you need to have a large dataset of images that contain license plate numbers. This dataset should contain various images, including pictures with different angles, lighting conditions, and distances. Second, you need to label each image in the dataset so that the CNN knows which areas of the image contain the license plate numbers. Finally, you need to train the CNN on this dataset so that it can learn to read the license plate numbers.
Using the correct number of cameras and positioning them accurately for optimal results can prove challenging, given the various missions and environments.
Creating a Real-time License Plate Detection and Recognition App
Training the Lpr Model
ANPR training models are designed to read and interpret license plate numbers from vehicles. This is done by analysing images of license plates and extracting the numbers from them. The training process typically involves feeding the model a large dataset of photos of license plates so that it can learn to recognise the patterns of numbers on them. Once the model is trained, it can be used to read license plates from images in real-time, which can be used for things like automatic toll payment or law enforcement.
Training Number Plates for ANPR
It’s probably not worth the effort! There are plenty of “off-the-shelf options already trained.
Prepare the Data
The first step is to prepare the data. This means ensuring that all of the images captured by the camera are in the same format. The images should also be cropped only to show the license plate itself. This step is important because it ensures that the machine-learning algorithm can read the license plate numbers accurately.
Label the Data
The next step is to label the data. This means assigning a unique identifier to each image. The identifier should be the number on the license plate. This step is crucial because it allows the machine learning algorithm to learn which numbers correspond to which license plates.
Train the Machine Learning Algorithm
The next step is to train the machine learning algorithm. This means feeding the algorithm the labelled data to learn how to recognise license plate numbers. This step is essential because it allows the algorithm to recognise license plate numbers in new images accurately.
Test the Machine Learning Algorithm
The final step is to test the machine learning algorithm. This means feeding the algorithm new images and seeing if it can accurately identify the license plate numbers. This step is important because it allows you to see how well the algorithm performs on real-world data.
Experiments Config
An ANPR engine is a complex task that requires an understanding of the many trade-offs involved in the design. The most critical design decisions include the choice of feature set, the size and shape of the region of interest, the choice of training data, and the number of LPR training images.
The first step in configuring an LPR engine is to choose the feature set. The most popular feature sets are Gabor filters and Haar-like features. Gabor filters are good at detecting edges, while Haar-like features detect flat regions.
The next step is to choose the size and shape of the region of interest. The area of interest is the part of the image that the ANPR engine will look at. The most common choices are a rectangle or a square.
After the region of interest has been chosen, the next step is to select the training data. The training data is a set of images that the LPR engine will use to learn how to recognise license plates. The most common training data is a set of pictures of license plates from different countries.
Finally, the last step is to choose the number of LPR training images. The number of training images should be large enough to provide a good representation of the different types of license plates but not so large that it takes too long to train the LPR engine.
Training
License plate recognition training is a process by which one can learn to identify and adequately interpret license plate numbers.
Aero Ranger Generation 3 with RTK and aviation-grade connectors.
This can be useful in several ways, such as for law enforcement or private security purposes. The process typically involves first being shown a variety of different license plates and then being tested on one's ability to identify the numbers correctly. With proper training, one can become quite adept at this task.
Exporting the Model
When you are satisfied with the performance of your model, you can export it as a TensorFlow SavedModel or HDF5 file. You can then serve the model using TensorFlow Serving or another tool, such as Apache MxNet Model Server.
To export the model, you need to specify the input and output nodes. The input node(s) feeds the model with data during inference. The output node(s) is used to get the model predictions. You can find the input and output nodes of your model by printing the model summary.
Once you have specified the input and output nodes, you can use the tf.estimator.export_saved_model() function to export the model.
The following code shows how to export the model to a TensorFlow SavedModel:
def serving_input_fn():
input_ph = tf.placeholder(tf.float32, [None, 784])
features = {'image': input_ph}
return tf.estimator.export.ServingInputReceiver(features, input_ph)
estimator.export_savedmodel('/tmp/mnist_saved_model', serving_input_fn)
To learn about all the options with model export, see the TAO Toolkit DetectNet_v2 documentation.
Other reading:
https://en.wikipedia.org/wiki/Automatic_number-plate_recognition
https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app/
farm security with cameras without wifi
license plate recognition online