Background Analysis With the development of social economy, the number of motor vehicles has increased sharply, and vehicle-related crimes have become more rampant and are increasing year by year, such as hit-and-run accidents, fake vehicle license plates, vehicle license plates, motor vehicle speeding, etc.
Although bayonet and electronic police systems, which are currently important technical means to deal with these problems, have curbed the occurrence of various illegalities to a certain extent, the system as a whole faces the following practical problems
Insufficient vehicle information is provided, and only basic data analysis of vehicle license plates is used
The retrieval accuracy of license plate recognition alone is low, and the accuracy of deployment and alarm is reduced
Data tracing efficiency is low, delaying the best time to solve the case
/>There is no efficient response to the illegal use of counterfeit vehicles
Currently dealing with counterfeit and counterfeit vehicles
Disadvantages and difficulties: occupying police resources, inefficiency and non-intelligence
System overview and characteristics
The vehicle identity analysis system is an intelligent vehicle identification system based on video stream and picture stream. It is the first full vehicle information identification system in China. It uses advanced machine learning and big data technology to automatically identify the license plate and vehicle model information for the public security traffic police. The department provides strong protection against illegal activities such as suspected counterfeit cards and decks.
Combining the world's leading machine learning and deep learning technologies, the identity of the vehicle can be identified, including license plate number, body color, vehicle brand, vehicle sub-model, specific year, etc. This engine supports multiple platforms, including platform platforms, etc., with rich and flexible interfaces. The functions of existing bayonet monitoring systems and high-definition monitoring system applications can be upgraded for users. Users need to provide platform data retrieval interfaces. Through the data retrieval interface, resources such as real-time captured pictures and videos of the bayonet can be retrieved for real-time or offline secondary recognition in the background.
License Plate Recognition
Supports recognition of license plate types: ordinary civilian blue plates, single-layer yellow plates, double-layer yellow plates, new armed police license plates, military plates, police license plates, agricultural license plates, Hong Kong license plates, etc.
Speed map
Recognition rate day, night
Car body color recognition
The car body color recognition module first obtains the position information of the car face, weights the color according to different positions, and finally outputs the body Dark and light colors, red, green, blue, pink, brown, yellow, white, black and gray and other common colors (supports output of one main color and one auxiliary color). Since color is seriously affected by lighting and nighttime fill light, the accuracy of vehicle body color recognition during the day is targeted.
Car model recognition
) The algorithm model recognition module uses a variety of feature fusion technologies to extract texture features such as car faces, and uses classifiers to identify car models.
) Supported types can identify the front-end pictures captured by the standard bayonet system and the front-end pictures captured by mobile phones. The rear-end recognition is being improved.
) Recognition types can currently identify a variety of vehicle sub-models of common vehicle brands. The types of vehicles are still increasing. Some of the vehicle brand names that can be recognized are as follows
) Recognition rate
Accurate car model recognition during the day
Good and accurate fill-in light at night
Comprehensive indicators
) Comprehensive speed: An ordinary desktop computer can process an average of 1 picture. A computer can open approximately one thread, so one machine can process one picture per day. Images can be processed in an hour.
) The recognition rate is for the vehicle model recognition module. Since the distribution of vehicle types in different application locations is different, our company provides automatic training and learning tools that can perform localized training to improve the accuracy of vehicle model recognition. The localization process is simple to operate and can be operated by the user or completed by the company's R&D personnel. In theory, the more vehicle brand samples collected, the higher the recognition rate.
) Recognition engine The vehicle identification engine supports multi-platform processing and can be placed on the front end or in the background for secondary identification.
Vehicle model identification