As the artificial intelligence market is hot, various industries are thinking about how to change. With the advent of the era of intelligent security, more and more security companies have begun to explore “artificial intelligence + security”, hoping to use artificial intelligence to make greater breakthroughs in security products and technologies. Since the biggest resource in the security industry is video images, and artificial intelligence has made great progress in image processing research, security is the industry with the highest degree of fit and the fastest falling speed in the field of artificial intelligence. At this stage, the most common applications of artificial intelligence in the security field are mainly license plate recognition and face recognition + Flap Barrier Gates. How will license plate recognition transform from principle to application?
The next focus of artificial intelligence is smart parking
The seemingly distant artificial intelligence has been developing rapidly in various fields in recent years. Taking the automotive industry as an example, since last year, Google’s self-driving cars have been frequently exposed by the media, and they have made great progress from internal testing to tens of thousands of kilometers of autonomous driving on the road, and people can even see them on the streets of some cities. Figures of self-driving cars. Once similar to the sci-fi movie “The Fifth Element” in the car owner lying in the car to the destination scene has been realized in parts of the United States.
Although artificial intelligence has made great strides in autonomous driving, it seems that its consideration of travel is not thoughtful enough. Because in addition to driving when people travel by car, the last link after destination setting-parking problem seems to have not been well solved. Just driving non-stop autonomous driving cannot complete the first closed loop of car travel. Hence the need for artificial intelligence in the parking industry.
In the face of parking problems that are no less difficult than driving, the emergence of “smart parking” has gradually made these imaginations possible. As a popular field under the Internet + O2O model, smart parking relies on a wide range of parking lot resources, and through the combination of hardware + software under the smart parking platform, the entire parking resources are intelligently integrated. At the same time, using technologies such as the Internet of Things, big data and cloud computing, a smart parking platform can be generated. Through this platform, each parking space is like a “singularity” and plays a role in the wisdom matrix of parking.
The technology of license plate recognition has been used in the security industry for a long time, and the technology is relatively mature. The application of artificial intelligence has improved the accuracy of license plate recognition. For manufacturers of license plate recognition algorithms, how to extend the recognition range of target vehicles and achieve more accurate recognition is what the market needs. In recent years, license plate recognition has been widely used in expressway vehicle management, and electronic toll collection (ETC) systems are also the main means of identifying vehicle identities combined with DSRC technology.
So how to choose a good license plate recognition system becomes the primary task to solve the problem of parking lot management. To evaluate a license plate recognition system technically, there are three indicators, namely recognition rate, recognition speed and background management system. Of course, the premise is that the system can run stably and reliably.
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1. The recognition rate of license plate recognition system
Whether a license plate recognition system is practical, the most important indicator is the recognition rate. The international transportation technology department has made a special discussion on the recognition rate index, and the requirement is that the correct recognition rate of all cards is 85% to 95% 24 hours a day. In order to test the recognition rate of a license plate recognition system, it is necessary to install the system in a practical application environment, run for more than 24 hours around the clock, collect at least 1,000 license plates for natural traffic flow for identification, and need to identify the license plate image and identification The results are stored for recall and viewing. Then, it is also necessary to obtain the actual passing vehicle images and the correct manual recognition results.
After that, the following recognition rates can be counted:
A. The recognition rate of natural traffic flow = the total number of correct recognitions of the whole license plate / the total number of vehicles actually passing through.
B. Percentage of identifiable license plates = total number of license plates read manually / total number of vehicles actually passing through.
C. The correct recognition rate of all recognizable license plates = the total number of license plates correctly recognized by all license plates / the total number of license plates read manually. These three indicators determine the recognition rate of the license plate recognition system, such as reliability and misrecognition rate. Intermediate results in the license plate recognition process.
2.the recognition speed of the license plate recognition system
The recognition speed determines whether a license plate recognition system can meet the requirements of real-time practical applications. If a system with a high recognition rate takes several seconds or even minutes to recognize the result, then the system will be useless because it cannot meet the real-time requirements in practical applications. For example, one of the functions of license plate recognition applications in expressway toll collection is to reduce the passing time, and speed is a powerful guarantee for reducing the passing time and avoiding traffic jams in this type of application. The recognition speed proposed by International Traffic Technology is within 1 second, the faster the better.
3. Background management of license plate recognition system
The background management system of a license plate recognition system determines whether the license plate recognition system is easy to use. It is important to realize that 100% recognition is not possible because the license plate is dirty, blurred, blocked, or the weather may be bad (snow, hail, fog, etc.).
Conclusion: There is no doubt that the automatic license plate recognition system is a relatively mature technology after years of development. In the future, license plate recognition technology will also be widely used, and the license plate recognition system industry will also face major changes. For companies focusing on the parking industry, only companies with independent core technologies and product quality standards can pass the test and truly The principle is applied to real life, which is also the only way for license plate recognition technology to move towards the stage of rapid development.
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