Vision-based displacement measurement sensor using modified Taylor approximation approach

Liu, Bingyou; Zhang, Dashan; Guo, Jie; Zhu, Changan

doi:10.1117/1.oe.55.11.114103

Cited by 18 publications

(4 citation statements)

References 40 publications

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“…The template algorithm did not able to the point in the above said frames; actual reason is to be studied. The references [25,26,[30][31][32][33][34][35][36][37][38][39][40][41][42][43] validate the sensor and algorithm of the proposed vision system. The sources [41 and 42] validate the outcomes and percentage of error.…”

Section: Figs14 and 15mentioning

confidence: 99%

Measurement of Hand-Arm Vibration Syndrome of an Agricultural Tractor Steering Wheel using Machine Vision System

2022

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The structure of an agricultural tractor is a structure without a suspension system to limit the vibrations generated by its engine. It exposed the entire body of an agricultural tractor operator to vibration. Thus, the body of the driver, constantly exposed to the vibration, becomes fatigued. This fatigue becomes a factor in the driver's inattention. The driver's body has transmitted to vibration through the tractor's seat, accelerator pedal, brake pedal, and steering wheel. Quantifying seat, accelerator pedal, brake pedal, and steering wheel vibrations is one of the most important factors in determining the cause of vibration and correcting it. The ISO 5349 standard lays down a guideline for the measurement and assessment of vibrations transmitted by the human hand. To meet the ISO 5349 standard, assess human exposure to hand-transmitted vibration and meet the human subjective comfort level. It must keep the vibration within a specified range. This study attempts to measure steering wheel vibrations through a new machine vision system using up-sampled cross correlation and a Discrete Finite Transform algorithm. In this study, it was impossible to calculate vibration in three axes from the video images recorded by the camera. According to ISO 5349-2, the vibration obtained on two axes multiplied by 1 to 1.7 to calculate the equivalent measurement for the vibration on the third axis. The results show that when the vibration is high; the errors are less and when the vibration is low, near zero, the errors range from 30 % to 50 %. But this study suggests that very low-level vibrations are unnecessary for vibration analysis. This study is not suitable for Nano-vibration measurement methods that measure fine vibrations.

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Section: Figs14 and 15mentioning

confidence: 99%

Measurement of Hand-Arm Vibration Syndrome of an Agricultural Tractor Steering Wheel using Machine Vision System

2022

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“…Dong et al (2019) utilized optical flow estimation to track non-target objects on grandstand structures and implemented modal identification. Some researchers also employed various template-matching algorithms to track and measure the displacement or vibrations of buildings (Lee et al 2017;Yin et al 2014;Liu et al 2016;Rajaram et al 2017;Chen et al 2017). Guo and Zhu (2016) applied the Lucas-Kanade template tracking algorithm on displacement measurement in an experiment, which is used as another method in this research.…”

Section: Related Workmentioning

confidence: 99%

Bridge vibration measurements using different camera placements and techniques of computer vision and deep learning

Bai,

Sezen,

Yilmaz

et al. 2023

ABEN

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In this paper, a new framework is proposed for monitoring the dynamic performance of bridges using three different camera placements and a few visual data processing techniques at low cost and high efficiency. A deep learning method validated by an optical flow approach for motion tracking is included in the framework. To verify it, videos taken by stationary cameras of two shaking table tests were processed at first. Then, the vibrations of six pedestrian bridges were measured using structure-mounted, remote, and drone-mounted cameras, respectively. Two techniques, displacement and frequency subtractions, are applied to remove systematic motions of cameras and to capture the natural frequencies of the tested structures. Measurements on these bridges were compared with the data from wireless accelerometers and structural analysis. Influences of critical parameters for camera setting and data processing, such as video frame rates, data window size, and data sampling rates, were also studied carefully. The research results show that the vibrations and frequencies of structures on the shaking tables and existing bridges can be captured accurately with the proposed framework. These camera placements and data processing techniques can be successfully used for monitoring their dynamic performance.

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“…For example, an ROI [22] and segmented screws [34] are used as natural targets. Compared to natural targets, many studies predefine artificial targets as the templates, such as concentric rings with a gradual blend from black to white at the edges [35][36][37], ArUco markers [3,9,38], coded and uncoded optical target arrays [39,40], circular border pattern with line pattern including multiple intersected lines [41][42][43][44], artificial quasi-interferogram fringe pattern (QIFP) [45], speckle pattern [46], illuminated light source [47], and retro reflective materials [48].…”

Section: Template Matchingmentioning

confidence: 99%

Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method

Nie

Bodda

Sandhu

et al. 2022

Sensors

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Computer-vision-based target tracking is a technology applied to a wide range of research areas, including structural vibration monitoring. However, current target tracking methods suffer from noise in digital image processing. In this paper, a new target tracking method based on the sparse optical flow technique is introduced for improving the accuracy in tracking the target, especially when the target has a large displacement. The proposed method utilizes the Oriented FAST and Rotated BRIEF (ORB) technique which is based on FAST (Features from Accelerated Segment Test), a feature detector, and BRIEF (Binary Robust Independent Elementary Features), a binary descriptor. ORB maintains a variety of keypoints and combines the multi-level strategy with an optical flow algorithm to search the keypoints with a large motion vector for tracking. Then, an outlier removal method based on Hamming distance and interquartile range (IQR) score is introduced to minimize the error. The proposed target tracking method is verified through a lab experiment—a three-story shear building structure subjected to various harmonic excitations. It is compared with existing sparse-optical-flow-based target tracking methods and target tracking methods based on three other types of techniques, i.e., feature matching, dense optical flow, and template matching. The results show that the performance of target tracking is greatly improved through the use of a multi-level strategy and the proposed outlier removal method. The proposed sparse-optical-flow-based target tracking method achieves the best accuracy compared to other existing target tracking methods.

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Vision-based displacement measurement sensor using modified Taylor approximation approach

Cited by 18 publications

References 40 publications

Measurement of Hand-Arm Vibration Syndrome of an Agricultural Tractor Steering Wheel using Machine Vision System

Measurement of Hand-Arm Vibration Syndrome of an Agricultural Tractor Steering Wheel using Machine Vision System

Bridge vibration measurements using different camera placements and techniques of computer vision and deep learning

Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method

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