SkyVision Platform Enables Efficient License Plate/Car Model Recognition

In the booming era of smart security systems, leveraging domestic AI chips to improve performance has become an industry hotspot. WeLinkirt's SkyVision zero-coding video surveillance AI platform demonstrates strong advantages in license plate/car model recognition scenarios.

User Scenario: The management of a parking lot at a transportation hub, as the customer, needs to accurately identify license plates and car models during the key process of vehicle entry and exit. The detection targets are various vehicles entering and leaving the parking lot, including cars of different brands, models, and colors, and the license plates also come in a variety of fonts, styles, and background colors.

Pain Points: Traditional license plate/car model recognition systems have many quantitative dilemmas. The miss-detection rate is relatively high, reaching about 5%. This means that one out of every 20 vehicles may not be correctly identified, leading to chaos in vehicle entry and exit management. The false-alarm rate is also not to be underestimated, about 8%. Frequent false alarms increase the workload of manual intervention and cause a waste of labor costs. Moreover, in the face of the trend of applying domestic AI chips, traditional systems are difficult to fully leverage the advantages of domestic chips to improve performance and cannot meet the growing demand for efficient and accurate recognition.

Technical Principle

The SkyVision zero-coding video surveillance AI platform uses advanced Convolutional Neural Network (CNN) algorithms. CNN has a powerful ability to extract image features and can automatically learn the key features of license plates and car models. In terms of imaging, the platform is connected to high-definition industrial cameras to ensure clear vehicle images are captured. In terms of hardware, it is deeply compatible with domestic AI chips, enabling it to fully utilize the computing power advantages of domestic chips for fast image analysis and feature matching. The reason it is effective is that the CNN algorithm can accurately identify license plate characters and car model feature patterns through extensive sample training, and the high-performance computing ability of domestic chips provides a guarantee for real-time large-scale data processing, making the entire recognition process efficient and accurate.

• The convolutional neural network can extract image features layer by layer, from low-level edge and texture features to high-level semantic features, gradually building an accurate understanding of license plates and car models.
• High - definition industrial cameras provide high-quality image input, laying the foundation for subsequent feature extraction and analysis.
• The parallel computing ability of domestic AI chips can process multiple image data simultaneously, greatly shortening the recognition time.
• The algorithm optimization and chip adaptation of the platform ensure stable operation in complex environments.

WeLinkirt Solution and Product

Centered around the SkyVision zero-coding video surveillance AI platform, this platform supports on - site training of its own models within hours. Administrators don't need to write code. They just need to upload a certain number of license plate and car model samples, and the model training can be completed in a short time to quickly adapt to different scenario requirements. At the same time, the platform has the function of behavior/event recognition and can judge vehicle entry and exit behaviors in real-time. Paired with an edge box, real-time alarms can be realized. Once an abnormal recognition situation occurs, an alarm will be immediately issued. In addition, the platform supports 100% local data storage without leaving the site, ensuring data security. Combined with the semantic understanding ability of the DaoAI World model, it can interpret and analyze license plate and car model information more accurately. Other product lines of WeLinkirt, such as the DaoAI AI AOI software system, can provide feature recognition support for visual basic models to assist in improving recognition accuracy, but the main core is still the SkyVision platform.

The SkyVision platform, with its zero-coding training and powerful recognition ability, is an ideal choice for license plate/car model recognition.

Quantitative Results: After using the SkyVision platform, the detection rate of license plate/car model recognition has been significantly increased to 98%, and the miss-detection rate has been reduced to <2%, effectively avoiding chaos in vehicle entry and exit management. The false-alarm rate has been reduced by -75%, from the original 8% to 2%, greatly reducing the workload of manual intervention and saving labor costs. Moreover, the model change time has been shortened to 2min, enabling it to quickly adapt to new license plate or car model style changes and improving the flexibility and adaptability of the system.