Labeling Service Case Study — Video Annotation — License Plate Recognition
Vehicle License Plate Recognition
Driven by the continuous innovation of “Ai + IoT” technology and application, a construction boom of “smart city” has been rising all over the world. As a key part of the construction, the intelligent transportation industry has been into the stage of rapid expansion.
Netvision Telecom is a technology company, located in South Korea, specializing in communication and network development. At present, the company undertakes the project of intelligent license plate recognition system of Incheon government in South Korea.
Vehicle license plate recognition (VLPR) is an application of computer video image recognition technology in vehicle license plate recognition. License plate recognition technology requires that the moving license plate can be extracted and recognized from the complex background. Through license plate extraction, image preprocessing, feature extraction, license plate character recognition and other technologies, information such as vehicle number and color can be recognized.
Video Annotation Case
To build such a system, firstly, it is necessary to have a large number of car license plate training data, clearly identifying and marking the plate type, background color, text, and number. Then, the recognition algorithm should be continuously trained with labeled pictures to improve the performance.
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Netvision Telecom chose to cooperate with ByteBridge to complete license plate number labeling projects.
ByteBridge firstly disassembles the complex tasks and divides them into several simple components, such as license plate counting, license plate labeling, unlabeled license plate counting, etc. Each part is aligned to a certain consensus mechanism to ensure accuracy.
Mathew Kim, head of the recognition program said, “it’s difficult to complete video annotation cases. The license plate is very small, and there is a lot to do. I didn’t expect that ByteBridge delivered the project in a very short time, with high accuracy and consistency. The overall rate reached 99%, which helped us solve a big problem.”
End
ByteBridge, a human-powered data labeling tooling platform with real-time workflow management, providing flexible data training service for the machine learning industry.
On the dashboard, clients can set labeling rules, iterate data features, attributes and workflow, scale up or down, make changes based on what they are learning about the model’s performance in each step of test and validation.
These labeling tools are available: Image Classification, 2D Boxing, Polygon, Cuboid.
Our expertise can create new recommendations based on the client’s use case. For further information, please visit our website site: ByteBridge
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Labeling Case Study — Video Annotation — License Plate Recognition was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
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