License Plate Recognition Parking Lot System - Parking Lot Equipment | HD License Plate Recognition

License plate recognition parking lot system - parking lot equipment | parking lot management equipment | parking lot system equipment, automatic license plate recognition principle, schematic diagram of license plate recognition process, schematic diagram of license plate recognition process. Automatic license plate recognition is a pattern recognition technology for automatic recognition of license plate number and color by using dynamic video or static image of vehicle. Its hardware foundation generally includes trigger equipment (monitoring whether the vehicle enters the field of view), camera equipment, lighting equipment, image acquisition equipment, processor for recognizing license plate number (such as computer), etc. its software core includes license plate location algorithm, license plate character segmentation algorithm and optical character recognition algorithm. Some license plate recognition systems also have the function of judging whether there is a car through video images, which is called video vehicle detection. A complete license plate recognition system should include vehicle detection, image acquisition, license plate recognition and so on.

When the vehicle detection section detects the arrival of the vehicle, it triggers the image acquisition unit to acquire the current video image. The license plate recognition unit processes the image, locates the license plate position, segments the characters in the license plate for recognition, and then forms the license plate number output. (1) Vehicle detection: vehicle detection can adopt buried coil detection, infrared detection, radar detection, video detection and other methods. Using video detection can avoid damaging the road surface, no additional external detection equipment, no need to correct the trigger position, save expenses, and is more suitable for mobile and portable applications. For video vehicle detection, the system needs to have high processing speed and adopt excellent algorithms to realize image acquisition and processing without losing frames. If the processing speed is slow, it will lead to frame loss, so that the system cannot detect the vehicle with fast driving speed.

At the same time, it is difficult to ensure that the recognition processing is started at the position conducive to recognition, which will affect the recognition rate of the system. Therefore, it is difficult to combine video vehicle detection with automatic license plate recognition. (2) License plate number and color recognition for license plate recognition. The following basic steps are required: 1) license plate positioning, positioning the license plate position in the picture; 2) License plate character segmentation, which divides the characters in the license plate; 3) License plate character recognition, recognize the segmented characters, and finally form the license plate number. In the process of license plate recognition, the recognition of license plate color is based on different algorithms and may be realized in the above different steps. It usually cooperates and verifies with license plate recognition. 1) In the natural environment of license plate location, the background of vehicle image is complex and the illumination is uneven.

How to accurately determine the license plate area in the natural background is the key of the whole recognition process. Firstly, a large-scale correlation search is carried out on the collected video image, and several regions in line with the characteristics of the vehicle license plate are found as candidate regions. Then these candidate regions are further analyzed and evaluated. Finally, a best region is selected as the license plate region and separated from the image. 2) After the license plate character segmentation completes the location of the license plate area, the license plate area is divided into a single character, and then recognized. Character segmentation generally adopts vertical projection method.

Since the projection of characters in the vertical direction must be near the local minimum value at the gap between or within characters, and this position shall meet the character writing format, characters, size restrictions and some other conditions of the license plate. The vertical projection method has a good effect on character segmentation in automobile image in complex environment. 3) At present, license plate character recognition methods mainly include template matching algorithm and artificial neural network algorithm. Based on the template matching algorithm, firstly, the segmented character is binarized and its size is scaled to the size of the template in the character database, then it is matched with all templates, and the best matching is selected as the result. There are two algorithms based on artificial neural network: one is to extract the features of characters, and then use the obtained features to train the neural network distributor; Another method is to input the image directly into the network, and the network automatically extracts the feature until the recognition result is obtained. In practical application, the recognition rate of license plate recognition system is also closely related to license plate quality and shooting quality.

The quality of license plate will be affected by various factors, such as rust, stain, paint peeling, font fading, license plate shielding, license plate inclination, highlight and reflection, multiple license plates, false license plates, etc; The actual shooting process will also be affected by environmental brightness, shooting mode, vehicle speed and so on. These factors reduce the recognition rate of license plate recognition to varying degrees, which is the difficulty and challenge of license plate recognition system. In order to improve the recognition rate, in addition to constantly improving the recognition algorithm, we should also find ways to overcome various lighting conditions to make the collected images most conducive to recognition.