矿用输送带双目视觉检测自适应标定方法

A self-adaptive calibration method for mine-used belt detection with binocular visio

  • 摘要: 针对煤矿输送带纵向撕裂视觉检测中,传统的摄像机标定方法复杂、精度低等问题,提出了一种高效的双目视觉检测自适应标定方法。分析了摄像机数学模型与双目视觉基本原理,在线性模型的基础上引入非线性畸变参数,将输送带图像与7×7矩阵模型融合提取出的关键特征角点坐标值代入矩阵约束方程,求解出摄像机内外参数及结构参数、畸变参数,并对其进行非线性优化,得到精确值,最后采用贝叶斯误差估计方法对计算出的参数与Faugeras自标定方法所得结果进行对比分析。实验结果表明该方法精度高,可靠性好。

     

    Abstract: For traditional complex camera calibration methods with low precision used for mine-used belt longitudinal tear detection, an efficient self-adaptive calibration method was proposed for binocular vision detection. A mathematical model of camera and fundamental principle of binocular vision were analyzed, and non-linear distortion parameters were introduced into the linear model. The coordinate values of feature corner points extracted from fused image combining belt image with a 7×7 matrix model are substituted into the matrix constraint equation. Furthermore, internal and external parameters, structure parameters and distortion parameters decomposed of the equation are non-linearly optimized to be more accurate. Finally, overall calculated parameters are compared with the ones obtained by Faugeras self-calibration method using Bayesian estimation error. The experimental results show that the method has high accuracy and reliability.

     

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