Global automatic detection method employing multi-level constraints for circular markers in high-speed videogrammetry

In recent years, algorithms for fast and accurate recognition and detection of circular markers have become crucial in the field of high-speed videogrammetry. However, most existing techniques often necessitate a manually selected region of interest that encompasses the full information of the circular marker. This manual box selection method is inefficient and unsuitable for practical engineering applications. To address this issue, we propose a global automatic recognition and detection approach that employs multi-level constraints for identifying circular markers in high-speed videogrammetry. First, an edge detection method based on the Canny algorithm is employed to extract candidate edges containing all circular markers. Subsequently, two geometric constraints—general geometric condition and roundness metric constraint—are applied to eliminate a large number of non-circular marker edges. Finally, pseudo-edges of circular markers are removed, and the corresponding accurate edges are retained by applying an extrema point condition constraint. The performance of the proposed method is evaluated using several high-speed videogrammetry image datasets. Experimental results demonstrated that our method can accurately detect and recognize all circular markers, outperforming comparable methods. The proposed method holds promise for efficient and wide-ranging applications in the recognition and detection of circular markers in high-speed videogrammetry.