May 18, 2024

Talking about the Trigger Mode of License Plate Recognition System

The license plate recognition system has two trigger modes, one is peripheral triggering and the other is video triggering.

Peripheral triggering refers to the use of coils, infrared or other detectors to detect vehicle passing signals. After license plate recognition system receives vehicle trigger signals, it collects images of vehicles, automatically recognizes license plates, and performs subsequent processing. This method has the advantages of high triggering rate and stable performance; the disadvantage is that the coils need to be laid on the ground for a large amount of construction.

The video triggering method means that the license plate recognition system adopts a dynamic moving target sequence image analysis processing technology to detect the movement of the vehicle in the lane in real time, and it is found that the vehicle captures the image of the vehicle as it passes through, identifies the vehicle license plate, and performs subsequent processing. Video triggering does not require coils, infrared or other hardware vehicle detectors. This method has the advantages of convenient construction, no need to cut the ground laying coils, and does not need to install car detectors and other components, but its disadvantages are also very significant. Due to the limit of the algorithm, the trigger rate and recognition rate of the program are more than Triggers are much lower.

1) Indirect method: Refers to identifying the license plate and related information by identifying the information of the license plate stored in the IC card or bar code installed on the car. IC card technology has high recognition accuracy, reliable operation, and can work around the clock, but its complete device is expensive, hardware equipment is very complex, and does not apply to remote operations; bar code technology has high recognition speed, high accuracy, high reliability and cost Low benefits, but high requirements for the scanner. In addition, both of them need to work out a unified national standard, and it is impossible to check if the car and the bar code match each other. This is also a technical disadvantage, which makes it difficult to promote in a short time.

2) Direct method: Image-based license plate recognition technology is a direct method. It is a passive type license plate intelligent identification method. It can be used in a moving-state vehicle or a stationary vehicle without any vehicle-mounted launcher that transmits dedicated license plate signals. The license plate number carries out non-contact information collection and intelligent identification in real time. Compared with the indirect method identification system, first of all, this system saves equipment placement and a large amount of funds, thereby improving the economic benefits; secondly, due to the advanced computer application technology, the recognition speed can be improved and the real-time performance can be better resolved. The problem; again, it is based on the image to identify, so through the person's participation can resolve the recognition error in the system, and other methods are difficult to interact with people.

Direct method generally has image processing technology, traditional pattern recognition technology and artificial neural network technology.

1) Image processing technology: The research of using image processing technology to solve vehicle license plate recognition began as early as 1980s, but at home and abroad, it only discusses one specific problem in license plate recognition, and usually only adopts simple image processing technology to solve it. , did not form a complete system system, the identification process is the use of industrial television cameras to take pictures of the front of the car, and then handed over to the computer for simple processing, and eventually still need manual intervention, such as the identification of the provinces of Chinese characters in the vehicle license plate, In 1985, someone used a common image processing technique to propose classification of Chinese character recognition based on the characteristics of the extracted characters. According to the projection histogram of the Chinese character, the floating closed value was selected, the peak value of the Chinese character in the vertical direction was extracted, and the tree shape was used. The table lookup method is used to perform rough classification of Chinese characters; then, based on the projection histogram of the Chinese characters in the horizontal direction, the appropriate closed value is selected and quantified to form a variable-length chain code, and then a dynamic programming method is used to find the standard pattern chain code. The minimum distance to achieve subdivision meters completes automatic recognition of the Chinese province name.

2) Traditional pattern recognition technology. Traditional pattern recognition techniques refer to structural features, statistical features, etc. In the 1990s, due to the development of computer vision technology, a systematic study of vehicle license plate recognition began to emerge. In 1990, AS.Johnson and others used computer vision technology and image processing technology to implement an automatic vehicle license plate recognition system. The system is divided into three parts: image segmentation, feature extraction, template construction, and character recognition. Using different histograms corresponding to different thresholds, a large number of statistical experiments determine the threshold range of the image histogram of the position of the license plate, so that the license plate is separated according to the histogram corresponding to the specific threshold, and then the standard character template preset is used to perform the mode. Matching recognizes characters.

3) Artificial neural network technology. In recent years, some countries with developed computers and related technologies have begun to discuss the use of artificial neural network technology to solve the problem of license plate recognition. For example, in 1994 MMMFANHY successfully used the BAM neural network method to automatically identify the characters on the license plate, BAM nerves. The network is a bi-directional associative single-layer network composed of the same neurons. Each character template corresponds to a unique BAM matrix, and the correct license plate number is identified by comparing with the characters on the license plate.

The disadvantage of adopting the BAM neural network method is that no mapping solves the problem of conflicting storage capacity and processing speed of the identification system.

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