Automatic speed control method based on visual detection

Abstract

The invention discloses an automatic speed control method based on visual detection. The automatic speed control method comprises the steps that 1, statistics is conducted on the positions of traffic signs in an image under the situation that the distance between a vehicle and a right side lane and the distance between the vehicle and a left side lane are different to obtain a table, a function f1 of the geometric deformation degree epsilon of the traffic signs and road corners theta and a function f2 of the critical speeds vcurlim of curves and the theta are established; 2, the edge line position L1 of the right side lane of the vehicle to be controlled and the position L2 away from a left-side vehicle are detected; 3, image areas including the traffic signs are selected from the image according to the L1, L2 and the table; 4, the road corners theta in the image are calculated by integrating two methods; 5, the geometric deformation degree epsilon of the traffic signs in the image are calculated according to the f1 and theta, the image is restored to detect speed limit signs and identify limit speed values v1; 6, the speed of the vehicle is controlled according to the relative speed of the vehicle in front, the speeds of vehicles on both sides in front, the v1 and the vcurlim. By the adoption of the automatic speed control method, the cost is saved while the system reliability is improved.

Description

technical field [0001] The invention belongs to the technical field of computer vision and the field of visual target detection, and relates to a vehicle speed automatic control method based on computer vision and image processing. Background technique [0002] With the development of intelligent transportation, driverless and driver assistance technologies, it is particularly important to improve the visual ability of cars. Most of the current research results focus on the detection and recognition of traffic signs, and then realize the speed adjustment, but it is still difficult to achieve high reliability in complex practical scenarios. [0003] At present, most traffic sign detection and recognition systems are based on color for preliminary detection, and then based on shape for further detection and positioning. [0004] The primary color-based detection methods are based on color clustering, multi-channel threshold segmentation (RGB+HIS, this method is the most widel...

AI Technical Summary

Problems solved by technology

[0008] 1. In the outdoor environment, affected by the actual road scene and its own driving conditions, the collected traffic signs are prone to deformation, which makes the accuracy rate not very high;

[0009] 2. The entire detection an

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