Vision-based Measurement System Research Articles

Radial vibration measurement of rotating shaft using constant density fringe pattern and line scan camera

The radial vibration signals of the rotor can provide abundant information about the health condition of the machine. In this paper, a simple vision-based measurement system is proposed to simultaneously measure two-dimensional displacements in radial directions for the rotating shaft, where the system consists of a constant density fringe pattern (CDFP), a line scan camera (LSC), and a lens. The CDFP should be installed around the surface of the rotating shaft to make the density of the fringe constant along the shaft axis, while the shaft axis is vertical to the optical axis of the LSC but not parallel to the line-array sensor of the LSC. Therefore, the density of the fringe imaged on the LSC is not constant because of the modulation of the circular surface of the shaft, and the distribution of the fringe density on the LSC is a U-shaped curve. Thus, the shaft centreline orbit can be tracked by the lowest point of the density distribution curve (DDC) of the fringe. Then, an efficient and accurate parameterized instantaneous frequency estimation method is employed to estimate the DDC of the fringe, because the variable density fringe can be regarded as an amplitude-modulated and frequency-modulated nonstationary signal whose instantaneous frequency function is equivalent to the DDC. Experimental results verify the feasibility and effectiveness of the proposed method by comparing it to the eddy current sensors.

Factors affecting the accuracy of a computer vision-based vehicle weight measurement system

Vehicle weight is an important element of traffic data as it affects both traffic safety and service life of infrastructures. Traditional techniques for vehicle weight measurement, such as static weighing and weigh-in-motion, are costly to maintain and have limited service life. Thus, the non-contact vehicle weight measurement has emerged as a promising alternative. This study proposes a computer vision (CV)-based method for vehicle weight measurement and the factors affecting its accuracy are analyzed. The process of CV-based weight measurement involves the capturing of tire (tyre) images, processing them to extract tire edges, calculating vertical tire deformations, determining actual tire pressures, and using the tire mechanics model to calculate the weight of each tire and the gross weight of the vehicle. Parametric studies are conducted to study the effects of factors such as distance between camera and tire, camera parameters, illumination, background, and vehicle speed on measurement accuracy. The results indicate that high accuracy is achieved under optimal conditions, such as short distance, appropriate camera parameters, normal illumination, large feature difference between tires and background, and low speed. In application, the identification error of gross vehicle weight is within 15% at 95% confidence interval, which is comparable to a weigh-in-motion system.

Bridge damage detection using precise vision-based displacement influence lines and weigh-in-motion devices: Experimental validation

This study presents an experimental validation for a high-precision vision-based Displacement Influence Line (DIL) measurement system for a purpose of damage detection on bridges. The vision-based DIL measurement system is a promising tool for structural health monitoring on real operation bridges, which combines two Computer Vision subsystems and weigh-in-motion (WIM) devices. Two vision systems are utilized for tracking vehicle position and measuring structural displacement, while the WIM device obtains vehicle weight information. To demonstrate the feasibility of such a vision-based DIL measurement system, this study developed a vision system using a Go-Pro camera for vehicle positioning and a consumer grade camera for displacement measurement, followed by a series of laboratory experiments on a simply supported bridge using vision-based DILs to assess damage existence and localisation. Five damage scenarios were created by restrengthening the test structure instead of damaging it. Each restrengthened structure was considered intact while the original structure was considered damaged. Vision-based DIL measurements were repeated 12 times for each damage scenario to observe uncertainties in damage localisation as well as DILs. As the measured DILs were found adversely affected by the friction on the boundary supports, the Chordwise Displacement Influence Line (cw-DIL) approach was proposed to compensate for this effect. Damage-induced cw-DILs were shown to be able to assess damage existence and localisation successfully and consistently for all five damage scenarios.

Reliability Assessment of a Vision-Based Dynamic Displacement Measurement System Using an Unmanned Aerial Vehicle.

In recent years, many studies have been conducted on the vision-based displacement measurement system using an unmanned aerial vehicle, which has been used in actual structure measurements. In this study, the dynamic measurement reliability of a vision-based displacement measurement system using an unmanned aerial vehicle was examined by measuring various vibrations with a frequency of 0 to 3 Hz and a displacement of 0 to 100 mm. Furthermore, free vibration was applied to model structures with one and two stories, and the response was measured to examine the accuracy of identifying structural dynamic characteristics. The vibration measurement results demonstrated that the vision-based displacement measurement system using an unmanned aerial vehicle has an average root mean square percentage error of 0.662% compared with the laser distance sensor in all experiments. However, the errors were relatively large in the displacement measurement of 10 mm or less regardless of the frequency. In the structure measurements, all sensors demonstrated the same mode frequency based on the accelerometer, and the damping ratios were extremely similar, except for the laser distance sensor measurement value of the two-story structure. Mode shape estimation was obtained and compared using the modal assurance criterion value compared with the accelerometer, and the values for the vision-based displacement measurement system using an unmanned aerial vehicle were close to 1. According to these results, the vision-based displacement measurement system using an unmanned aerial vehicle demonstrated results similar to those of conventional displacement sensors and can thus replace conventional displacement sensors.

A Lightweight Vision-Based Measurement for Hand Gesture Information Acquisition

The utilization of hand gesture recognition (HGR) can achieve a simpler and more humanized human–computer interaction (HCI) system, which can be used to simplify some aspects of production life and can also be used for sign language interpretation robots. However, HGR has a highly customized design. It is a very time-consuming process to design and train a separate model for each set of datasets. Therefore, designing a hand gesture information (HGI) acquisition instrument to analyze the rigid body motion and internal posture of the hand at the same time is a necessary process. This article presents a lightweight vision-based measurement (VBM) system to achieve this function. The system is implemented based on the convolutional neural network (CNN) structure, the improved Kalman filter (KF) is used to compensate error of fast moving, and the testing effect is good. After testing, the improved hand position detection system has the highest recall rate in the single-stage network. Compared with the original you look only once (YOLO)-v3, the number of parameters is reduced by 46.7M, but the recall rate is increased by 0.28%, which solves the loss of hand detection under complex background. With the help of the improved KF algorithm, the average precision (AP) metric of the prediction box is improved by 2.0 compared with the previous one. Compared with the simple baseline, the area under curve (AUC) metric of the keypoints detector is improved by 0.5%.

A Vision-Based Displacement Measurement System for Foundation Pit

A Vision-Based Displacement Measurement System for Foundation Pit

Stereo Vision-Based Measurement System for Geometric Parameters of High-Speed Railway Catenary

Stereo Vision-Based Measurement System for Geometric Parameters of High-Speed Railway Catenary

Development of a low-cost vision-based real-time displacement system using Raspberry Pi

Vision-based real-time displacement measurement systems can measure the structural displacements without contact and output the results immediately, overcoming some drawbacks of traditional contact-type sensors and off-line vision sensors in field applications. However, high cost and complexity of installation restrict the widespread application of the off-the-shelf vision-based real-time displacement systems. In this work, a fast image processing software is developed for operation in single board computers (SBC), firstly, based which a low-cost and portable vision-based real-time displacement measurement system using Raspberry Pi is constructed for civil structural health monitoring. To evaluate the performance of the system in field applications, a new method for validation of on-site measurement accuracy is proposed. The experimental results show that the constructed system accurately measured multipoint structural displacements of a long-span suspension bridge in real-time, with at least 1/17 pixel accuracy, providing a cost-efficient, convenient, high-precision, and contactless approach to obtain the displacement responses of infrastructures in the field.

Mixed Event-Frame Vision System for Daytime Preceding Vehicle Taillight Signal Measurement Using Event-Based Neuromorphic Vision Sensor

An important aspect of the perception system for intelligent vehicles is the detection and signal measurement of vehicle taillights. In this work, we present a novel vision-based measurement (VBM) system, using an event-based neuromorphic vision sensor, which is able to detect and measure the vehicle taillight signal robustly. To the best of our knowledge, it is for the first time the neuromorphic vision sensor is paid attention to for utilizing in the field of vehicle taillight signal measurement. The event-based neuromorphic vision sensor is a bioinspired sensor that records pixel-level intensity changes, called events, as well as the whole picture of the scene. The events naturally respond to illumination changes (such as the ON and OFF state of taillights) in the scene with very low latency. Moreover, the property of a higher dynamic range increases the sensor sensitivity and performance in poor lighting conditions. In this paper, we consider an event-driven solution to measure vehicle taillight signals. In contrast to most existing work that relies purely on standard frame-based cameras for the taillight signal measurement, the presented mixed event/frame system extracts the frequency domain features from the spatial and temporal signal of each taillight region and measures the taillight signal by combining the active-pixel sensor (APS) frames and dynamic vision sensor (DVS) events. A thresholding algorithm and a learned classifier are proposed to jointly achieve the brake-light and turn-light signal measurement. Experiments with real traffic scenes demonstrate the performance of measuring taillight signals under different traffic conditions with a single event-based neuromorphic vision sensor. The results show the high potential of the event-based neuromorphic vision sensor being used for optical signal measurement applications, especially in dynamic environments.

Use of a roving computer vision system to compare anomaly detection techniques for health monitoring of bridges

Displacement measurements can provide valuable insights into structural conditions and in-service behaviour of bridges under operational and environmental loadings. Computer vision systems have been validated as a means of displacement estimation; the research developed here is intended to form the basis of a real-time damage detection system. This paper demonstrates a solution for detecting damage to a bridge from displacement measurements using a roving vision sensor-based approach. Displacements are measured using a synchronised multi-camera vision-based measurement system. The performance of the system is evaluated in a series of controlled laboratory tests. For damage detection, five unsupervised anomaly detection techniques: Autoencoder, K-Nearest Neighbours, Kernel Density, Local Outlier Factor and Isolation Forest, are compared. The results obtained for damage detection and localisation are promising, with an f1-Score of 0.96–0.97 obtained across various analysis scenarios. The approaches proposed in this research provide a means of detecting changes to bridges using low-cost technologies requiring minimal sensor installation and reducing sources of error and allowing for rating of bridge structures.

Vision-based fringe projection measurement system for radial vibration monitoring of rotating shafts

Shaft radial vibration signals of rotary machines are crucial dynamic information for condition monitoring and fault diagnosis. A novel vision-based fringe projection vibration measurement system is proposed to monitor the radial displacements of a rotating shaft. The fringe pattern was projected on the surface of the shaft and the deformed fringe was acquired by a high-speed camera. A novel fringe processing algorithm was proposed to extract the radial displacement with good accuracy and efficiency. The vertical displacement could be obtained by locating the symmetric center of the recorded fringe. Meanwhile, the horizontal displacement could be obtained from the shift of the fringe at the symmetric center. The influence factors affecting measurement performance were analyzed. Simulations and comparative experiments were carried out for the verification of the feasibility and reliability of the proposed system. Comparing with the multiple eddy current sensors based system, the proposed method can realize multi-dimensional vibration measurement using one camera. The merits of simple in system, easy in operation, high accuracy and effectiveness in measurement make it attractive for dynamic characterization, condition monitoring and faults diagnosis of rotating machineries in actual applications.

Vision-Based Methods for Relative Sag Measurement of Suspension Bridge Cables

Main cables, comprising a number of wire strands, constitute a vital element in long-span suspension bridges. The determination of their alignment during construction is of great importance, and relative sag is commonly measured for the efficient sag adjustment of general strands. The conventional approach uses the caterpillar method, which is inconvenient, difficult-to-implement, and potentially dangerous. In order to realize the high-precision measurement of cable alignment in a strong wind environment, a vision-based method for relative sag measurement of the general cable strands is proposed in this paper. In the proposed measurement system, images of pre-installed optical targets are collected and analyzed to realize the remote, automatic, and real-time measurement of the relative sag. The influences of wind-induced cable shaking and camera shaking on the accuracy of the height difference measurement are also theoretically analyzed. The results show that cable strand torsion and camera roll have a great impact on the measurement accuracy, while the impacts of the cable strand swing and vibration, camera swing and vibration, and camera pitch and yaw are insignificant. The vision-based measurement system tested in the field experiment also shows a measurement error within 3 mm, which meets the requirements for cable adjustment construction. At the same time, the vision-based measurement method proposed and validated in this paper can improve the measurement accuracy and efficiency of strand alignment in a strong wind environment. Potential risks involved in the manual measurement, e.g., working at heights and in strong wind environments, can be eliminated, facilitating the automation of the cable erection process.

A Shape Reconstruction and Measurement Method for Spherical Hedges Using Binocular Vision.

The center coordinate and radius of the spherical hedges are the basic phenotypic features for automatic pruning. A binocular vision-based shape reconstruction and measurement system for front-end vision information gaining are built in this paper. Parallel binocular cameras are used as the detectors. The 2D coordinate sequence of target spherical hedges is obtained by region segmentation and object extraction process. Then, a stereo correcting algorithm is conducted to keep two cameras to be parallel. Also, an improved semi-global block matching (SGBM) algorithm is studied to get a disparity map. According to the disparity map and parallel structure of the binocular vision system, the 3D point cloud of the target is obtained. Based on this, the center coordinate and radius of the spherical hedges can be measured. Laboratory and outdoor tests on shape reconstruction and measurement are conducted. In the detection range of 2,000–2,600 mm, laboratory test shows that the average error and average relative error of standard spherical hedges radius are 1.58 mm and 0.53%, respectively; the average location deviation of the center coordinate of spherical hedges is 15.92 mm. The outdoor test shows that the average error and average relative error of spherical hedges radius by the proposed system are 4.02 mm and 0.44%, respectively; the average location deviation of the center coordinate of spherical hedges is 18.29 mm. This study provides important technical support for phenotypic feature detection in the study of automatic trimming.

Vision-Based Cable Displacement Measurement Using Side View Video.

Recent tragedies around the world have shown how accidents in the cable-stayed bridges can wreak havoc on the society. To ensure the safety of the cable-stayed bridges, several studies have estimated the cable tension force using the vibration of cables. Most of these methods for estimating the tension of a cable start with measuring the displacement of the cable. Recent development of commercial cameras provide opportunity for more convenient and efficient method for measuring the displacement of cable. However, traditional vision-based displacement measurement methods require the assumption that the movement of the cable should be measured in parallel to the camera plane. This assumption limits the installation location of the camera when measuring the displacement of a cable. Therefore, this study introduces a new vision-based cable displacement measurement system that can measure the displacement of a cable in various locations even when the camera is installed in the side of the cable. The proposed method consists of three phases: (1) camera projection matrix estimation, (2) cable tracking in the image coordinate, and (3) cable displacement estimation in the world coordinate. To validate the performance of the proposed method, a simulation-based validation test, a lab-scale validation test, and an on-site validation test were conducted. The simulation-based validation test verified the performance of the proposed method in an ideal condition, and the lab-scale validation test showed the performance of the method in physical environment. Finally, the on-site validation test showed that the proposed method can measure the cable displacement with a side view camera.

Precision Motion Measurement for Linear Motor Based on FNCC and Phase Difference M-Estimator Robust Regression in a VLSM System

This paper investigates a low-cost vision-based line-scan measurement (VLSM) system to achieve the high-precision positioning of linear motors. And an improved two-step phase difference method is performed for detecting subpixel translation of 1-D multi-frame images. In the first step of the algorithm, a fast normalized cross-correlation (FNCC) is adopted for coarse estimation to extract the overlapping portions of the two frames. In the second step, M-Estimator robust regression method is used to estimate the displacement parameters of the phase difference matrix of the overlap further accurately. The simulation cases indicate that the proposed approach outperforms the other existing methods in accuracy and robustness. In addition, the system and proposed algorithm are effectively validated in experiments and achieve a displacement detection of 0.01 mm grade of the mover.

A Mobile Robot with Omnidirectional Tracks—Design and Experimental Research

This article deals with the design and testing of mobile robots equipped with drive systems based on omnidirectional tracks. These are new mobile systems that combine the advantages of a typical track drive with the advantages of systems equipped with omnidirectional Mecanum wheels. The omnidirectional tracks allow the robot to move in any direction without having to change the orientation of its body. The mobile robot market (automated construction machinery, mobile handle robots, mobile platforms, etc.) constantly calls for improvements in the manoeuvrability of vehicles. Omnidirectional drive technology can meet such requirements. The main aim of the work is to create a mobile robot that is capable of omnidirectional movement over different terrains, and also to conduct an experimental study of the robot’s operation. The paper presents the construction and principles of operation of a small robot equipped with omnidirectional tracks. The robot’s construction and control system, and also a prototype made with FDM technology, are described. The trajectory parameters of the robot’s operation along the main and transverse axes were measured on a test stand equipped with a vision-based measurement system. The results of the experimental research became the basis for the development and experimental verification of a static method of correcting deviations in movement trajectory.

IoT-enabled computer vision-based parts inspection system for SME 4.0

This paper presents a Computer vision-based Measurement (CViM) system for three-dimensional (3D) mechanical parts. The system integrates with internet of things (IoT) and demonstrates a low cost and modular implementation of SME 4.0 framework. A complete system design including hardware and software is presented. Three cameras are used to capture various images that expose different dimensions of the mechanical part. Back lighting is used to illuminate the part placed on the V-shaped translucent surface, which results in images with improved contrast thereby improving the measurement accuracy compared to front lighting. The software algorithm performs camera calibration to eliminate the lens distortion and pre-processing for image enhancement and noise elimination. Next, image binarization is performed to highlight the edges of the object. The desired dimensions are extracted from the image followed by pixel to physical measurement mapping. The system is tested by measuring physical dimensions of a drill bit and a screwdriver bit and it achieves an accuracy of tenth of a millimeter (mm). Several dimensions of the test parts are measured, such as, total length, back radius, groove length etc., which are the most number of dimensions considered, compared to previous studies. The results demonstrate that the maximum difference between the measured and the actual dimensions is ± 0.05 mm. A conceptual model of IoT-enabled CViM system for parts inspection in small and medium-sized industries is also presented. The system has a fairly low cost and is very suitable for such industries with similar accuracy requirements for their parts inspection.

Cost-effective, vision-based multi-target tracking approach for structural health monitoring

The displacement response of structures is an important parameter in structural health monitoring (SHM). Displacement responses can be applied in both structural performance monitoring and structural dynamic characteristics monitoring. To overcome the shortcomings of traditional contact sensors, a vision-based multi-point structural displacement measurement system equipped with an inexpensive surveillance camera and a consumer camera was developed herein. In addition, to reduce the computing time of target tracking, an improved region-matching algorithm based on the prior knowledge of structural deformation was proposed. Numerical results revealed that the improved region-matching algorithm could save computing time without reducing location accuracy. Moreover, static and dynamic loading tests were conducted on a scale model of a suspension bridge to validate the effectiveness of the proposed vision-based measurement system. Displacement responses and modal parameters obtained from the vision-based measurement system were compared with those of traditional contact sensors, and a satisfactory consistency was obtained. Hence, the proposed vision-based measurement system could be a cost-effective alternative to conventional displacement sensors and accelerometers for SHM.

Machine vision-based relative-angle measurement system between circular holes

In this study, the machine-vision technique is employed to measure the angle between two circular holes for the derailleur parts of a bicycle. A delta robot equipped with a motorized zoom lens is constructed as an automatic measurement device. In this way, we can detect the normal vectors of circular holes with different positions and sizes by using only one camera. By calculating the plane normal vectors of two circular holes within the camera coordinate system and performing the cross-product operation, the angle between the two circular holes is obtained. Simulation experiments reveal that the measurement method is influenced by the projection eccentricity error, and the error can be reduced by increasing the focal length and employing the virtual-center alignment method. The results show that the average error for measuring the derailleur parts is approximately 0.061°, and the standard deviation is 0.112°. Experimental analysis indicates that while the magnification of the image is larger, the average error of the measurement result is reduced, and the standard deviation is reduced as well. When the magnification is 0.4×, the measurement average error is 0.04°, and standard deviation is 0.101°. When the distance between the center of the circle and the center of the image is increased, the error in the angle calculation is also increased. A significant improvement is obtained by setting the fitting center position to the optical central position, which omits the influence of the error amplification caused by the distance of the circular hole from the optical axis.

Robust high precision multi-frame motion detection for PMLSMs’ mover based on local upsampling moving least square method

This paper studies a multi-frame visual sub-pixel measurement technique to achieve the precise positioning of the permanent magnet linear synchronous motor (PMLSM) rapidly. A new matrix multiplication discrete Fourier transform (DFT) upsampling moving least square (MLS) algorithm is proposed to improve the accuracy and robustness of PMLSM positioning. Firstly, a line-scan vision-based measurement system is designed, and an aperiodic sinusoidal fringe image is placed on the pedestal as target image. Secondly, the local upsampling cross-correlation curve of adjacent one-dimensional signals is calculated to obtain sub-pixel displacement using the matrix-multiplied DFT method. Finally, to further improve the displacement accuracy, the MLS method is applied. Simulation tests verify that the proposed method is superior to other algorithms in measurement accuracy, calculation efficiency and measurement range. The experiment results confirm the feasibility and validity of the proposed method for detecting the mover motion under different working environments.