Using lightweight convolutional neural network to track vibration displacement in rotating body video. (1st September 2022)

• Record Type: Journal Article
• Authors: Yang, Rongliang; Wang, Sen; Wu, Xing; Liu, Tao; Liu, Xiaoqin
• Is Part Of: Mechanical systems and signal processing. Volume 177(2022)
• Publication Date: 2022-09-01
• Subjects: Rotating structure -- Vibration displacement measurement -- Deep learning -- Lightweight convolution neural networks -- Fuzzy target -- Target detection and tracking; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.109137
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: The non-contact visual displacement measurement methods can detect problems such as rigid body deformation and structural wear of structural bodies due to long-term service life. However, the traditional visual vibration measurement methods are limited by the inherent sampling constraint of ordinary image sensors, which cannot achieve efficient vibration target identification and correlation tracking for rotating body targets in low-resolution videos under the premise of high sampling rate. Therefore, this paper uses high-speed industrial camera as the acquisition medium, introduces deep convolutional neural networks into the field of visual vibration measurement, on this basis, weighs the target detection accuracy and displacement tracking speed, uses the designed lightweight convolutional neural networks model to vibration displacement tracking in rotating body videos. We ensure that the computational efficiency and parameter quantity of the convolutional networks are solved with low loss accuracy. First of all, we take the lightweight convolutional neural network as the backbone network, replace the standard convolutional neural network with depthwise separable convolution and pointwise convolution. Considering the speed and accuracy advantages of deep learning algorithms for video object tracking, we estimate the heat map, object center offsets and bounding box sizes use an anchor-free detection algorithm on the network framework. In order to prevent the loss of vibrating target identity in rotating body videos, we use re-identification(re-ID) method to strengthen the correlation of target displacement between adjacent frames. In experiments with traditional visual vibration measurement and recent deep learning measurement methods for performance testing, the network model we designed can demonstrate absolute advantages that traditional convolutional neural networks do not have. On the one hand, our comparison of time–frequency characteristics at different speeds shows that the vibration displacement curve regressed by the lightweight convolutional neural network has a high degree of fit with the displacement signal obtained by the eddy current sensor;on the other hand, when the target object is blurred, the generalization ability of our algorithm is proved, which also reflects the engineering application value of visual vibration measurement in the field of vibration displacement tracking of rotating bodies. Graphical abstract: Highlights: The high-speed camera is used as a vision sensor, measures the vibration displacement of rotating body. Based on the lightweight convolutional neural network, build a tracking by detection algorithms. Data comparison with Visual algorithms and eddy current sensor verifies the efficiency and accuracy of our method. Our network can still better regress the vibration displacement signal when dealing with ambiguous scenes.