A simulated roadway recognition method based on statistical valve sectioning

Abstract

The invention discloses a simulated lane recognition method based on statistical threshold segmentation and acquires image information of the racetrack in front of a racing car by simulating image acquisition module of the car. The invention is characterized in that the method includes judging the fringe condition of the black line row by row according to the racetrack image information, doing difference construction on pixel values of any two neighboring data points in one row of the racetrack image, judging whether the two points are the black line fringe in the row according to whether the absolute value of the difference is larger than the black-white demarcation threshold and judging whether the black line fringe is white on the left and black on the right or black on the left and white on the right according to the sign of the difference value, and then extracting the range of the black line racetrack integrating the condition of each row of black line fringe in the racetrack image information. The technical proposal of the invention can quickly and accurately decide the position of the front racetrack and, with image threshold segmentation, can avoid interference of shades and light and also overcomes the problem that the computation complexity is too high in treatment and analysis modules in the prior art. The invention has excellent real-time performance and especially applies to racetrack recognition simulation of small cars.

Description

technical field [0001] The invention belongs to the field of intelligent identification, and in particular relates to a method for identifying a simulated lane based on statistical threshold segmentation. Background technique [0002] In various intelligent simulation car competitions, a key issue is how to effectively and accurately determine the position of the black line track, so as to ensure that the simulated car runs along the track correctly. [0003] The simulated car recognizes the black line track by taking real-time pictures of the front track through the image acquisition module (usually a camera is installed above the front of the car), and then sends the captured image to the processing and analysis module of the simulated car for image analysis to determine the black line race. Finally, the corresponding control strategy is adopted to control the simulated car to drive along the black line track. [0004] However, in the actual design process of the simulati...

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Problems solved by technology

[0004] However, in the actual design process of the simulation car, two aspects must be considered. One is the impact of different directions of light on the image quality of the black line track captured by the camera. This impact is sometimes very serious.

If the light is too strong, the black line in the image captured by the camera will have the same color as the white area next to it, so that the processing and analysis module of the simulated car cannot accurately analyze the position of the black line track; on the other hand, the camera The impact on the quality of the captured images cannot be ignored, and the quality of images of the front track captured by cameras of different natures is also different.

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