Rapid detection algorithms for log diameter classes based on binocular vision
CHEN Guanghua
ZHANG Qiang
CHEN Meiqian
LI Jianwei
YIN Huaiyong
Abstract：Aimed at the key problem that automatic detection of log piles diameter classes,by the binocular stereo vision and image segmentation principle,3D information of log-end is determined rapidly.According to the histogram feature of log-end,a region labeling method based on the maximum entropy threshold segmentation is presented,which sets the dynamic threshold to achieve the accurate segmentation of the log-end area and background.Meanwhile,with the help of the ORB feature point detection method,combined the epipolar geometry theory with stereo matching,the 3D coordinates are obtained rapidly.Otherwise taking the log piles as the detection object,the least squares principle is fitted to get the best fitting ellipse and log diameter class parameters of major axis and minor axis.Experiment shows that the proposed algorithms can detect the log diameter classes in 10 s,and the measurement error is in the 2 mm.
Keywords：pattern recognitionlog diameter classbinocular visionimage identificationellipse fitting
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 22-30 )