Adaptive-filtering-based rapid multi-circle detection method for image under complex background

Abstract

The invention discloses an adaptive-filtering-based rapid multi-circle detection method for an image under a complex background, and belongs to the technical field of computer vision online detection. The Sage-Husa adaptive kalman filtering algorithm is adopted for estimating the center coordinate and radius of each circle feature so as to estimate feature valid areas; in the feature valid area of each circle, after local enhancement of the image, the Canny edge detection algorithm and the least-squares ellipse fitting method are adopted for solving the center coordinate and radius of the circle feature; whether a positioning result is valid is judged according to a result validity criterion; if yes, a feature extraction value is output as a detection result; if not, a feature parameter estimation value is output as a detection result. Time-consuming Hough transformation is thoroughly abandoned in the multi-circle positioning process under the complex background, whether the positioning result is valid is judged before the measuring result is output, different measures are taken for different judgment results, the circle detection speed is substantially increased while precision is guaranteed, and the online monitoring requirement of an industrial monitoring system with a high-definition image sensor can be met.

Description

technical field [0001] The invention relates to the technical field of computer vision online detection, in particular to a multi-circle fast detection method under complex background based on adaptive filtering. Background technique [0002] Computer vision technology has the advantages of non-contact, economy, flexibility and integration, and has broad application prospects in the field of industrial testing and online inspection. Circle detection is one of the key steps in many computer vision measurement systems. In some test systems that have emerged in recent years, computer vision algorithms are required to achieve fast and accurate multi-circle extraction in high-resolution complex backgrounds. [0003] Traditional circle detection methods include template method, center of gravity method, moment estimation method, curve fitting method and Hough transform method, etc. Among them, only Hough transform method can realize multi-circle positioning in complex background, ...

AI Technical Summary

Problems solved by technology

However, the above improvements are still based on the slow Hough transform. With the development of hardware technology, the resolution of industrial inspection images is getting higher and higher. When these methods are used for high-resolution inspection images, the time consumption will increase rapidly, and the real-time performance is poor. remains an outstanding issue

Images