激光与光电子学进展, 2020, 57 (10): 101103, 网络出版: 2020-05-08   

基于双目视觉与特征匹配跟踪的薄壁件振动测量 下载: 1020次

Vibration Measurement of Thin-walled Parts Based on Binocular Vision and Matching and Tracking of Features
作者单位
湖南科技大学机械设备健康维护湖南省重点实验室, 湖南 湘潭 411201
摘要
针对薄壁件振动测量的需求,将双目视觉与特征匹配跟踪结合,提出了一种较为准确的薄壁件振动测量方法。先利用双目相机连续采集薄壁件的振动图像,并进行滤波和二值化等图像预处理操作;再选取左右相机拍摄的第一帧图像,根据极线约束原理对图像上的特征点进行立体匹配;利用改进后的光流法对第一帧图像上的特征点进行跟踪,从而获得第二帧到最后一帧图像上特征点准确的像素坐标;最后根据双目视觉测量原理获得物体的三维振动位移信息。实验研究和分析结果表明,该方法能够准确测量薄壁件的振动位移信息,为进一步开展振动特性分析、减振优化设计和结构损伤识别等研究提供一种新的技术参考。
Abstract
Aim

ing at the requirement of vibration measurement of thin-walled parts, the binocular vision is combined with the matching and tracking of features to realize a more accurate method for measuring the vibration of thin-walled parts. First, the vibration images of thin-walled parts were continuously collected using a binocular camera, and image pre-processing operations, such as filtration and binarization, were performed. Second, the first frame image captured using the left and right cameras was selected, and the feature points on the image were stereo-matched according to the principle of epipolar constraint. Then, the improved optical flow method was used to track the feature points of the first frame image to obtain accurate pixel coordinates of the feature points from the second to the last frame images. Finally, the three-dimensional vibration displacement information of the object was obtained based on the binocular vision measurement principle. Experimental research and analysis show that the proposed method can accurately extract the vibration displacement information of thin-walled parts, thereby providing a new technical reference for further research on vibration characteristic analysis, vibration-damping optimization design, and structural damage identification.

伍济钢, 邵俊, 周根, 阳德强, 成远. 基于双目视觉与特征匹配跟踪的薄壁件振动测量[J]. 激光与光电子学进展, 2020, 57(10): 101103. Jigang Wu, Jun Shao, Gen Zhou, Deqiang Yang, Yuan Cheng. Vibration Measurement of Thin-walled Parts Based on Binocular Vision and Matching and Tracking of Features[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101103.

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