Curve detection method and device

Abstract

The invention discloses a curve detection method and device. The method comprises the following steps: sampling foreground points in a foreground point set to obtain a sample set, wherein samples in the sample set comprise a first preset number of foreground points; obtaining a curve corresponding to the samples according to the positions of the foreground points contained in the samples; obtaining adaptive values of the samples according to the relative position of each foreground point in the foreground point set and the curve; and determining a target curve according to the adaptive value of each sample in the sample set. Compared with a traditional algorithm, the method has the advantages that the time complexity and the space complexity of the curve detection method are greatly reduced, the speed is high, and real-time operation can be achieved. In addition, the curve detection precision is high, and the optimal solution can be found out with great probability. In a parabolic detection scene, compared with a curve detection method based on Hough transform and a curve detection method based on a least square method, the method provided by the invention can maintain the detection precision and has better robustness, so that the method has obvious advantages.

Description

technical field [0001] The present application relates to the technical field of computer vision, in particular to a curve detection method and device. Background technique [0002] In the field of computer vision technology, recognizing object features is a very critical research. In application scenarios such as target tracking, target positioning, product quality control, and factory automation, curve detection is required based on sensor detection results. [0003] An existing curve detection algorithm is the curve detection based on the Hough transform. This algorithm converts the curve detection problem from the coordinate space of the point to the parameter space of the curve equation. In practical applications, the parameter space is too large and the space and events are complicated. The problem of too high detection accuracy is high but the running speed is slow, so it is difficult to run in real time. Another existing curve detection algorithm is the curve detec...

AI Technical Summary

Problems solved by technology

This algorithm regards the curve fitting problem as a linear regression problem and uses the least square method to solve it. It is fast, but the detection accuracy is low and it is easily affected by noise.

From the above two detection methods, it is not difficult to find that it is difficult to balance the accuracy and speed problems when performing curve detection, which makes it impossible to achieve fast and accurate curve detection.

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