Use case: how ANPR data optimizes license plate recognition
Automatic license plate recognition (otherwise called “ANPR” for”Automatic Number Plate Detection“) is a technology widely adopted in various sectors, especially in car park management, to automate and simplify vehicle identification. Automatic license plate recognition systems (or “ALPR”) for”Automatic License Plate Reading“) offer significant advantages for access management and security, thanks to their ability to identify vehicles via their registration number.
Artificial intelligence (AI) plays a key role in improving the efficiency and accuracy of these systems based on machine learning algorithms and specialized data sets.
This data, collected and annotated rigorously, allows models to better understand and interpret the visual characteristics of plates, even under difficult conditions. In this article, we tell you more about this artificial intelligence use case!
What is automatic license plate recognition (ANPR)?
Automatic license plate recognition (ANPR) is a technology based on computer vision and artificial intelligence, designed to detect, read, and record vehicle license plates.
The system uses cameras and sophisticated algorithms to analyze captured images, extract relevant information (such as the characters on a plate), and convert it into digital data. This allows the rapid identification of vehicles, without human intervention, facilitating applications such as parking management, road checks, or even automatic tolls.
ANPR generally works in three steps: image capture, processing to locate the plate in the image, and finally, character recognition using algorithms of optical character recognition (OCR). This process must be robust to work in varied environments (poor lighting, vehicle speed, different shooting angles) while ensuring high accuracy.
Its increasing adoption is due to its ability to automate repetitive tasks and improve the efficiency of traffic and safety management systems.
How does AI improve the accuracy of ANPR systems?
Artificial intelligence (AI) improves the accuracy of automatic license plate recognition (ANPR) systems through advanced machine learning and computer vision algorithms. It is a key technology in parking management and security, offering benefits such as improving operational efficiency and reducing costs through process automation.
Here are several ways in which AI is helping to make these systems more efficient:
Image processing
AI models can analyze license plate images with high accuracy, even under difficult conditions such as poor lighting, damaged plates, or non-ideal shooting angles.
Deep learning algorithms, in particular, are capable of extracting complex features from images, making it possible to better recognize plaques in a variety of environments.
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Machine learning
The AI uses annotated ANPR data sets to train models to recognize different license plate configurations, taking into account variations in plate style, size, and format across countries or regions. The more the system is exposed to various examples, the more accurate it becomes in recognition.
Correction of errors
Using techniques such as fault detection and information validation, AI can correct possible errors when reading plates. For example, if a letter or number is poorly recognized, the AI can use probabilistic algorithms to propose corrections based on common plate models.
Adapting to changing conditions
AI systems are able to adapt to environmental variations (such as rain, fog, or night) by adjusting image processing parameters in real time. This allows them to maintain high accuracy regardless of external conditions.
Continuous improvement
One of the big benefits of AI is its ability to learn new cases. By constantly collecting new data, ANPR models become more accurate over time, improving the ability of systems to correctly identify plaques, even in novel situations. RAPI systems make it easier to identify vehicles and eliminate the need for physical access, increasing safety and comfort for users.
By integrating these learning and adaptation capabilities, AI makes it possible to optimize ANPR systems by making them more robust, reliable and accurate, which is essential in critical contexts such as public safety and traffic management.
What about labelled data to train ANPR detection models?
To train ANPR detection models, labelled data plays a leading role in improving the accuracy and robustness of systems. This data consists of rigorously annotated license plate images, including variations in plate styles, formats, and sizes across regions, as well as different environmental contexts (low light, varying angles, weather conditions).
Annotations provide accurate landmarks that allow machine learning models to identify specific characteristics of plates, even under adverse conditions. By training models with diverse and high-quality datasets, ANPR systems become more efficient and more adaptive, ensuring reliable detection and reading in a variety of contexts, from car parks to public roads.
What are the most common use cases of ANPR in car park management?
The use of automatic license plate recognition in car park management has become increasingly common, offering automated solutions to improve efficiency and the user experience. License plate recognition, also known as LPR, optimizes car park management by reducing costs, improving the user experience, and facilitating operations such as barrier-free entry and exit. Here are some of the most common use cases of ANPR in this field:
Automated entry and exit
ANPR makes it possible to automate the entry and exit of vehicles in car parks. It is an effective solution to automate these processes, thus improving the management of car parks. As the vehicle approaches the entrance, the camera captures the license plate image, which is then recognized and allows access automatically if the vehicle is registered in the system (subscribers or pre-registered visitors). This eliminates the need for physical tickets or access cards.
Automated payment
By integrating ANPR into payment systems, drivers can enter and leave car parks without having to interact with a payment terminal. The license plate is linked to a payment account or bank card, which allows automatic debit according to the length of parking, thus offering a smooth experience for users.
Subscription Management
Car parks that offer monthly or annual subscriptions use ANPR to automatically manage the access of subscribed vehicles. Subscriber plates are registered in the system, allowing frictionless access during the subscription validity period. This not only improves the subscribers' experience, but also reduces the need for manual management.
Surveillance of illegal parking
The ANPR is also used to monitor unauthorized parking or exceeding the authorized time. ANPR cameras can detect and report vehicles that have not paid for their parking or that have exceeded the authorized time, allowing parking operators to react quickly, whether by sending a ticket or a notification.
Parking space reservation
Some car parks offer online reservation systems where drivers can reserve a space in advance. The ANPR is then used to ensure that the reserved space is allocated only to the vehicle corresponding to the license plate provided during the reservation, ensuring effective management of the available spaces.
Optimization of seat occupancy
The ANPR makes it possible to monitor the occupancy of parking spaces in real time by detecting the vehicles present. This makes it possible to better manage space, to direct users to free spaces, or to optimize traffic flows in car parks.
Security and access control
ANPR systems are also used to increase security in car parks. They make it possible to prohibit access to unauthorized or potentially suspicious vehicles by comparing license plates with databases of stolen or wanted vehicles. This is particularly useful in car parks for companies or in sensitive areas.
These use cases show how ANPR can make car park management more fluid, secure and efficient, while reducing operational costs associated with manual processes.
Conclusion
Automatic license plate recognition (ANPR), combined with artificial intelligence, is transforming car park management by offering effective, automated and reliable solutions. Through the use of carefully annotated datasets, AI models can accurately identify license plates, even under complex conditions.
Whether it is to automate the entry and exit of vehicles, optimize payments or strengthen security, ANPR continues to demonstrate its usefulness in a wide range of applications. As this technology evolves, it promises to make parking management even more fluid and intelligent, while adapting to new challenges and requirements.
