Non-contact vibration sensor using deep learning and image processing. (October 2021)

• Record Type: Journal Article
• Authors: Guo, Jinquan; Wu, Xinran; Liu, Jiantao; Wei, Tieping; Yang, Xiaoxiang; Yang, Xinyi; He, Bingwei; Zhang, Weihao
• Is Part Of: Measurement. Volume 183(2021)
• Publication Date: 2021-10
• Subjects: Vibration measurement -- Optical flow -- Deep learning -- Convolutional neural network -- Photogrammetry -- Computer vision -- Non-contact measurement; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109823
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A new method for non-contact measurement of vibration frequency. Use deep learning methods to replace traditional edge detection methods. Optical flow method can extract the vibration time history of the object. A comparison of measurement accuracy between proposed methods and sensors. Abstract: This paper proposes a non-contact vibration measurement method based on deep learning and image processing. The deep learning method is used to realize the automatic and efficient selection of effective pixels and the optical flow method is used to extract vibration signals to realize non-contact and targetless visual vibration measurement. In this study, a carbon plate board and aluminum C-beam structure were measured and verified under artificial and non-human excitation in a laboratory environment. Additionally, bridge and cable structures in an outdoor environment were selected as measurement targets to verify the reliability of the proposed method. This paper compares the experimental results of Canny and Sobel edge detection algorithms and deep learning methods to verify the efficiency of deep learning. The results demonstrate that our method is robust, even under real-world unfavorable conditions, meaning it can serve as a novel measurement method in the field of vibration measurement.