管道图像透视失真校正方法

Zheng Zhang, Jiazheng Zhou, Xiuhong Li, Chaobin Xu, Xinyu Hu, Linhuang Wang
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摘要

在采用全景图像解包方法进行中直径管道缺陷检测时,经常会发现管道图像存在严重的透视畸变,导致图像解包和拼接质量低下,这是由于摄像机的光轴完全偏离管道中心造成的。为解决这一问题,提出了一种减少管道图像透视畸变的新型校正方法,用于管道缺陷检测。首先,该方法增强了管道内不均匀照明区域的边缘,以方便图像分割和识别校正透视畸变所需的关键点。然后,针对圆形目标提出了六特征点提取方法,以建立提取特征点与参考圆上映射点之间的投影关系。最后,构建了一个透视矩阵,以完成对扭曲图像的透视变换校正。结果表明,所提矫正方法的平均矫正率和平均相对误差分别达到了 90.85% 和 1.31%。该研究创新性地利用不均匀光照的增强作用来寻找扭曲的边缘信息。它提出了一种利用参考圆和六个关键特征点建立映射模型的提取方法。该研究提供了一种新颖的方法,可用于获得优质的管道检测图像,为后续高质量管道图像拼接奠定坚实基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Correction Method for Perspective Distortions of Pipeline Images
It is common to find severe perspective distortion in a pipeline’s image in medium-diameter pipeline defect detection by the panoramic image unwrapping method, resulting in low-quality image unwrapping and stitching, which is caused by the camera’s optical axis being completely deviated from the pipeline’s center. To solve this problem, a novel correction method for reducing perspective distortion in pipeline images was proposed for pipeline defect detection. Firstly, the method enhances the edges of unevenly illuminated regions within a pipeline to facilitate image segmentation and identify key points necessary for correcting perspective distortion. Then, a six-feature-point extraction method was proposed for a circle target to establish the projection relationship between the extracted feature and mapped points on the reference circle. Finally, a perspective matrix was constructed to complete the perspective transformation correction of the distorted images. The results show that the average correction rate and the average relative error of the proposed correction method can reach 90.85% and 1.31%, respectively. The study innovatively used the enhancement of uneven illumination to find distorted edge information. It proposed an extraction method using a reference circle and six key feature points to build a mapping model. It can provide a novel method which can be used to obtain a superior image for pipeline detection and lay a solid foundation for subsequent high-quality pipeline image stitching.
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