Artificial Neural Network for Vibration Frequency Measurement Using Kinect V2

被引:0
作者
Liu, Jiantao [1 ]
Yang, Xiaoxiang [1 ,2 ]
机构
[1] Fuzhou Univ, Sch Mech Engn & Automat, Fuzhou 350108, Fujian, Peoples R China
[2] Quanzhou Normal Univ, Quanzhou 362000, Fujian, Peoples R China
基金
中国国家自然科学基金;
关键词
Optical data processing - Optical variables measurement;
D O I
10.1155/2019/9064830
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Optical measurement can substantially reduce the required amount of labor and simplify the measurement process. Furthermore, the optical measurement method can provide full-field measurement results of the target object without affecting the physical properties of the measurement target, such as stiffness, mass, or damping. The advent of consumer grade depth cameras, such as the Microsoft Kinect, Intel RealSence, and ASUS Xtion, has attracted significant research attention owing to their availability and robustness in sampling depth information. This paper presents an effective method employing the Kinect sensor V2 and an artificial neural network for vibration frequency measurement. Experiments were conducted to verify the performance of the proposed method. The proposed method can provide good frequency prediction within acceptable accuracy compared to an industrial vibrometer, with the advantages of contactless process and easy pipeline implementation.
引用
收藏
页数:16
相关论文
共 36 条
  • [1] [Anonymous], 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
  • [2] [Anonymous], 2017, P NIPS 2017 AUT WORK
  • [3] [Anonymous], 2011, RGB‐D Workshop on 3D Perception in Robotics at the European Robotics Forum
  • [4] Beberniss T., 2012, P 25 INT C NOIS VIBR
  • [5] Validity of the Microsoft Kinect for assessment of postural control
    Clark, Ross A.
    Pua, Yong-Hao
    Fortin, Karine
    Ritchie, Callan
    Webster, Kate E.
    Denehy, Linda
    Bryant, Adam L.
    [J]. GAIT & POSTURE, 2012, 36 (03) : 372 - 377
  • [6] Accuracy evaluation of sub-pixel structural vibration measurements through optical flow analysis of a video sequence
    Diamond, D. H.
    Heyns, P. S.
    Oberholster, A. J.
    [J]. MEASUREMENT, 2017, 95 : 166 - 172
  • [8] A Vision-Based Sensor for Noncontact Structural Displacement Measurement
    Feng, Dongming
    Feng, Maria Q.
    Ozer, Ekin
    Fukuda, Yoshio
    [J]. SENSORS, 2015, 15 (07): : 16557 - 16575
  • [9] A combined temporal tracking and stereo-correlation technique for accurate measurement of 3D displacements: application to sheet metal forming
    Garcia, D
    Orteu, JJ
    Penazzi, L
    [J]. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2002, 125 : 736 - 742
  • [10] Deep Residual Learning for Image Recognition
    He, Kaiming
    Zhang, Xiangyu
    Ren, Shaoqing
    Sun, Jian
    [J]. 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 770 - 778