综采工作面多视觉全局坐标系研究

Multi-vision global coordinate system in fully mechanized coal mining face

  • 摘要: 现阶段综采工作面分布的摄像仪较分散,且煤矿智能化应用多需大场景且高分辨率的图像,井下广角视觉相机与全景相机的引用试图恢复尽可能宽广的综采工作面视野,但是由于设备从单一测点进行图像采集,获得的图像会出现畸变,甚至图像失真,因此有必要进行多视觉全局坐标系的技术研究。介绍图像采集装置的安装设计,使其满足所采集图像有一定重叠且特征稳定,探讨该布置方式下的图像标定方法,利用最小二乘法的方式能更好的进行摄像仪模型的标定,拟合精度高且鲁棒性低。通过对工作面图像进行灰度范围压缩或拉伸,提出一种改进型直方图均衡化方法,提高了工作面低照度情况下的图像细节体现与部分强光下的抑制效果,使得井下环境的辨识度更高,优化其图片质量。通过比较目前比较成熟的特征点提取算法,最终采用Surf算法进行特征点的匹配与提取,分配特征点的主方向并完成特征点的描述子。针对该算法出现的误匹配特征点较多的情况,研究消除误匹配特征点的RANSIC算法,并通过角点检测配对的方式提高了有价值的特征点数量。最终通过坐标点坐标建立透视矩阵表达式与全局坐标系从而完成图像的拼接,采用加权平滑的方法对拼接的图像进行去裂缝处理,最终实现多视觉图片的快速有效融合。试验表明该方法可有效增强井下图像细节,去除井下图像误匹配特征点,可快速消除拼接的裂缝,并能快速准确的完成两、三幅图像的拼接融合。该方法具有误差小、效率高且拼接速度快等特点,使拼接图像满足综采工作面图像应用的层次,应用范围与前景广阔。

     

    Abstract: Currently the distribution of cameras at fully mechanized working face is dispersed,and the mine intelligent applications need more big scene and high-resolution images. The wide-angle vision camera and panoramic camera for underground mines try to restore the broad field of vision for fully mechanized working face as much as possible. How- ever,due to the equipment from a single point in image acquisition,image distortion will appear,and even result in los- ing image fidelity. Therefore,it is necessary to do more visual technology research on the global coordinate system. This paper introduces the installation and design of the image acquisition device to make it meet the requirement that the collected images are overlapped to a certain extent and the features are stable. An improved histogram equalization method is proposed by compressing or stretching the gray scale range of the working face image,which can improve the image detail reflection under low illumination and the suppression effect under partial strong light,so as to improve the identification of underground environment and optimize the image quality. Surf algorithm is used to match and extract feature points,allocate the main direction of feature points and complete the description of feature points. Aiming at the situation that there are many mismatched feature points in this algorithm,RANSIC algorithm to eliminate mismatched feature points is studied,and the number of valuable feature points is increased by means of corner point detection and pairing. Finally,the perspective matrix expression and the global coordinate system are established through the coordi- nate point coordinates to complete the image splicing,and the weighted smoothing method is adopted to remove the cracks in the spliced images,so as to realize the fast and effective fusion of multi-vision images. Experiments show that this method can effectively enhance underground mine image details,remove underground mine image mismatching feature points,quickly eliminate stitching cracks,and quickly and accurately complete two or three images stitching fusion.

     

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