Train number automatic identification method and system
An automatic recognition system and automatic recognition technology, applied in the field of image recognition, can solve the problems of inconsistent grayscale of the same character, no analysis and processing, etc., and achieve the effect of saving processing time, reducing the degree of manual dependence, and achieving a high degree of intelligence.
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Embodiment 1
[0050] The car number automatic identification system of this embodiment includes a camera 2, a light source 3, a wheel sensor 1 and a processing unit, and the installation diagram of the camera 2 and the light source 3 is as follows figure 2 As shown, camera 2 is a 5-megapixel industrial CCD camera with a delay time of 1500 μS and an exposure time of 2000 μS. The light source 3 is an LED flash light source. The single power of the light source 3 is 250W. There are 2 cameras 2 and 3 light sources respectively. The distance between the light source 3 and the ground is equidistant, the distances between the two cameras 2 and the ground are unequal distances, placed up and down, the width of the box 4 is 30cm, the height is 40cm, the height of the bottom of the box 4 is 2.6m from the ground, and the box 4 consists of a Supported by pillar 5, the processing unit is a vehicle number positioning and recognition system.
Embodiment 2
[0052] The on-site installation schematic diagram of the automatic vehicle number identification system of this embodiment is as follows image 3 As shown, the shooting device is installed on one side of the track, the camera 2 and the light source 3 are packaged in a closed box 4, the box 4 is supported by a pillar 5, and the distance between the pillar 5 and the center of the track is 2.8m, so The wheel sensor 1 transmits a trigger signal to the camera 2 and the light source 3, and the camera 2 transmits the image information to the processing unit.
Embodiment 3
[0054] The processing unit, that is, the workflow of the vehicle number positioning and recognition system is as follows: Figure 4 As shown, it is mainly divided into image preprocessing, rough positioning of vehicle number image, precise positioning of vehicle number image, tilt correction of vehicle number image, segmentation of vehicle number image and recognition of vehicle number image.
[0055] Specifically, the principle flowchart of the positioning system in the processing unit is as follows Figure 5 As shown, the steps include:
[0056] Step 1: Image preprocessing, crop the image according to the approximate area where the vehicle number appears to obtain the ROI area, extract the color components according to the difference between the color of the vehicle number and the background of the vehicle body, and turn the color image into a single-channel image;
[0057] Step 2: Use the local binarization method based on the mean and variance to eliminate some interferen...
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