A Binocular Vision-Based Crack Detection and Measurement Method Incorporating Semantic Segmentation.

The morphological characteristics of a crack serve as crucial indicators for rating the condition of the concrete bridge components. Previous studies have predominantly employed deep learning techniques for pixel-level crack detection, while occasionally incorporating monocular devices to quantify the crack dimensions. However, the practical implementation of such methods with the assistance of robots or unmanned aerial vehicles (UAVs) is severely hindered due to their restrictions in frontal image acquisition at known distances. To explore a non-contact inspection approach with enhanced flexibility, efficiency and accuracy, a binocular stereo vision-based method incorporating full convolutional network (FCN) is proposed for detecting and measuring cracks. Firstly, our FCN leverages the benefits of the encoder-decoder architecture to enable precise crack segmentation while simultaneously emphasizing edge details at a rate of approximately four pictures per second in a database that is dominated by complex background cracks. The training results demonstrate a precision of 83.85%, a recall of 85.74% and an F1 score of 84.14%. Secondly, the utilization of binocular stereo vision improves the shooting flexibility and streamlines the image acquisition process. Furthermore, the introduction of a central projection scheme achieves reliable three-dimensional (3D) reconstruction of the crack morphology, effectively avoiding mismatches between the two views and providing more comprehensive dimensional depiction for cracks. An experimental test is also conducted on cracked concrete specimens, where the relative measurement error in crack width ranges from -3.9% to 36.0%, indicating the practical feasibility of our proposed method.

Design and Metrological Analysis of a Backlit Vision System for Surface Roughness Measurements of Turned Parts.

The focus of this study is to design a backlit vision instrument capable of measuring surface roughness and to discuss its metrological performance compared to traditional measurement instruments. The instrument is a non-contact high-magnification imaging system characterized by short inspection time which opens the perspective of in-line implementation. We combined the use of the modulation transfer function to evaluate the imaging conditions of an electrically tunable lens to obtain an optimally focused image. We prepared a set of turned steel samples with different roughness in the range Ra 2.4 µm to 15.1 µm. The layout of the instrument is presented, including a discussion on how optimal imaging conditions were obtained. The paper describes the comparison performed on measurements collected with the vision system designed in this work and state-of-the-art instruments. A comparison of the results of the backlit system depends on the values of surface roughness considered; while at larger values of roughness the offset increases, the results are compatible with the ones of the stylus at lower values of roughness. In fact, the error bands are superimposed by at least 58% based on the cases analyzed.

Non-Contact Measurement and Identification Method of Large Flexible Space Structures in Low Characteristic Scenes.

The end-operation accuracy of the satellite-borne robotic arm is closely related to the satellite attitude control accuracy, and the influence of the vibration of the satellite's flexural structure on the satellite attitude control is not negligible. Therefore, a stable and reliable vibration frequency identification method of the satellite flexural structure is needed. Different from the traditional non-contact measurement and identification methods of large flexible space structures based on marker points or edge corner points, the condition of non-marker points relying on texture features can identify more feature points, but there are problems such as low recognition and poor matching of features. Given this, the concept of 'the comprehensive matching parameter' of scenes is proposed to describe the scene characteristics of non-contact optical measurement from the two dimensions of recognition and matching. The basic connotation and evaluation index of the concept are also given in the paper. Guided by this theory, the recognition accuracy and matching uniqueness of features can be improved by means of equivalent spatial transformation and novel relative position relationship descriptor. The above problems in non-contact measurement technology can be solved only through algorithm improvement without adding hardware devices. On this basis, the Eigensystem Realization Algorithm (ERA) method is used to obtain the modal parameters of the large flexible space structure. Finally, the effectiveness and superiority of the proposed method are verified by mathematical simulation and ground testing.

Liquid Mixing on Falling Films: Marker-Free, Molecule-Sensitive 3D Mapping Using Raman Imaging.

Following up on a proof of concept, this publication presents a new method for mixing mapping on falling liquid films. On falling liquid films, different surfaces, plain or structured, are common. Regarding mixing of different components, the surface has a significant effect on its capabilities and performance. The presented approach combines marker-free and molecule-sensitive measurements with cross-section mapping to emphasize the mixing capabilities of different surfaces. As an example of the mixing capabilities on falling films, the mixing of sodium sulfate with tap water is presented, followed by a comparison between a plain surface and a pillow plate. The method relies upon point-by-point Raman imaging with a custom-built high-working-distance, low-depth-of-focus probe. To compensate for the long-time measurements, the continuous plant is in its steady state, which means the local mixing state is constant, and the differences are based on the liquids' position on the falling film, not on time. Starting with two separate streams, the mixing progresses by falling down the surface. In conclusion, Raman imaging is capable of monitoring mixing without any film disturbance and provides detailed information on liquid flow in falling films.

Computer Vision Technology for Monitoring of Indoor and Outdoor Environments and HVAC Equipment: A Review.

Artificial intelligence technologies such as computer vision (CV), machine learning, Internet of Things (IoT), and robotics have advanced rapidly in recent years. The new technologies provide non-contact measurements in three areas: indoor environmental monitoring, outdoor environ-mental monitoring, and equipment monitoring. This paper summarizes the specific applications of non-contact measurement based on infrared images and visible images in the areas of personnel skin temperature, position posture, the urban physical environment, building construction safety, and equipment operation status. At the same time, the challenges and opportunities associated with the application of CV technology are anticipated.

Non-contact detection method of pregnant sows backfat thickness based on two-dimensional images.

Since sow backfat thickness (BFT) is highly correlated with its service life and reproductive effectiveness, dynamic monitoring of BFT is a critical component of large-scale sow farm productivity. Existing contact measures of sow BFT have their problems including, high measurement intensity and sows' stress reaction, low biological safety, and difficulty in meeting the requirements for multiple measurements. This article presents a two-dimensional (2D) image-based approach for determining the BFT of pregnant sows when combined with the backfat growth rate (BGR). The 2D image features of sows extracted by convolutional neural networks (CNN) and the artificially defined phenotypic features of sows such as hip width, hip height, body length, hip height-width ratio, length-width ratio, and waist-hip ratio, were used respectively, combined with BGR, to construct a prediction model for sow BFT using support vector regression (SVR). Following testing and comparison, it was shown that using CNN to extract features from images could effectively replace artificially defined features, BGR contributed to the model's accuracy improvement. The CNN-BGR-SVR model performed the best, with R2 of 0.72 and mean absolute error of 1.21 mm, and root mean square error of 1.50 mm, and mean absolute percentage error of 7.57%. The results demonstrated that the CNN-BGR-SVR model based on 2D images was capable of detecting sow BFT, establishing a new reference for non-contact sow BFT detection technology.

Quantification and Visualization of Reliable Hemodynamics Evaluation Based on Non-Contact Arteriovenous Fistula Measurement.

The condition of arteriovenous fistula (AVF) blood flow is typically checked by using auscultation; however, auscultation should require a qualitative judgment dependent on the skills of doctors, and further attention to contact infection is required. For these reasons, this study developed a non-contact and non-invasive medical device to measure the pulse wave of AVFs by applying optical imaging technology. As a first step toward realization of the quantification judgment based on non-contact AVF measurement, we experimentally validated the developed system, whereby the hemodynamics of 168 subjects were visually and quantitatively evaluated based on clinical tests. Based on the evaluation results, the fundamental statistical characteristics of the non-contact measurement, including the average and median values, and distribution of measured signal-to-noise power ratio, were demonstrated. The clinical test results contributed to the future construction of quantified criteria for the AVF condition with the non-contact measurement.

Non-Contact Measurement of Motion Sickness Using Pupillary Rhythms from an Infrared Camera.

Both physiological and neurological mechanisms are reflected in pupillary rhythms via neural pathways between the brain and pupil nerves. This study aims to interpret the phenomenon of motion sickness such as fatigue, anxiety, nausea and disorientation using these mechanisms and to develop an advanced non-contact measurement method from an infrared webcam. Twenty-four volunteers (12 females) experienced virtual reality content through both two-dimensional and head-mounted device interpretations. An irregular pattern of the pupillary rhythms, demonstrated by an increasing mean and standard deviation of pupil diameter and decreasing pupillary rhythm coherence ratio, was revealed after the participants experienced motion sickness. The motion sickness was induced while watching the head-mounted device as compared to the two-dimensional virtual reality, with the motion sickness strongly related to the visual information processing load. In addition, the proposed method was verified using a new experimental dataset for 23 participants (11 females), with a classification performance of 89.6% (n = 48) and 80.4% (n = 46) for training and test sets using a support vector machine with a radial basis function kernel, respectively. The proposed method was proven to be capable of quantitatively measuring and monitoring motion sickness in real-time in a simple, economical and contactless manner using an infrared camera.

Non-Contact Monitoring of Human Vital Signs Using FMCW Millimeter Wave Radar in the 120 GHz Band.

A non-contact heartbeat/respiratory rate monitoring system was designed using narrow beam millimeter wave radar. Equipped with a special low sidelobe and small-sized antenna lens at the front end of the receiving and transmitting antennas in the 120 GHz band of frequency-modulated continuous-wave (FMCW) system, this sensor system realizes the narrow beam control of radar, reduces the interference caused by the reflection of other objects in the measurement background, improves the signal-to-clutter ratio (SCR) of the intermediate frequency signal (IF), and reduces the complexity of the subsequent signal processing. In order to solve the problem that the accuracy of heart rate is easy to be interfered with by respiratory harmonics, an adaptive notch filter was applied to filter respiratory harmonics. Meanwhile, the heart rate obtained by fast Fourier transform (FFT) was modified by using the ratio of adjacent elements, which helped to improve the accuracy of heart rate detection. The experimental results show that when the monitoring system is 1 m away from the human body, the probability of respiratory rate detection error within ±2 times for eight volunteers can reach 90.48%, and the detection accuracy of the heart rate can reach 90.54%. Finally, short-term heart rate measurement was realized by means of improved empirical mode decomposition and fast independent component analysis algorithm.

A method of crack detection based on digital image correlation for simulated cracked tooth.

BACKGROUND: Early clinical cracked tooth can be a perplexing disorder to diagnose and manage. One of the key problems for the diagnosis of the cracked tooth is the detection of the location of the surface crack. METHODS: This paper proposes an image-based method for the detection of the micro-crack in the simulated cracked tooth. A homemade three-axis motion platform mounted with a telecentric lens was built as an image acquisition system to observe the surface of the simulated cracked tooth, which was under compression with a magnitude of the masticatory force. By using digital image correlation (DIC), the deformation map for the crown surface of the cracked tooth was calculated. Through image analysis, the micro-crack was quantitatively visualized and characterized. RESULTS: The skeleton of the crack path was successfully extracted from the image of the principal strain field, which was further verified by the image from micro-CT. Based on crack kinematics, the crack opening displacement was quantitatively calculated to be 2-10 µm under the normal mastication stress, which was in good agreement with the value reported in the literature. CONCLUSIONS: The crack on the surface of the simulated cracked tooth could be detected based on the proposed DIC-based method. The proposed method may provide a new solution for the rapid clinical diagnosis of cracked teeth and the calculated crack information would be helpful for the subsequent clinical treatment of cracked teeth.

Viaxl: A Solution of a Low-Cost Real-Time Visual Accelerometer Based on Laser Speckle Optical Flow Detection.

Non-contact and non-destructive acceleration measurement is receiving considerable attention due to their low cost, flexibility, and simplicity of implementation, as well as their excellent performance in some emerging applications such as medical electronics applications, vibration monitoring, and some other special scenarios. In this paper, a visual accelerometer system based on laser speckle optical flow detection named Viaxl is proposed. Compared with the conventional non-contact acceleration measurement method based on a laser system, Viaxl has moderate and stable performance with the advantages of low cost and simplicity of implementation. Experiment results demonstrate that Viaxl, which consists of a commercial camera and a low-cost laser pointer, can achieve real-time, non-contact acceleration measurement, and confirm the basic system performance of Viaxl: a measurement nonlinearity better than 1.3%, up to 31 dB signal-to-noise ratio, and 1150 Hz theoretic bandwidth; this demonstrates the huge potential of Viaxl in a wide range of applications, and provides a new possible technical method for non-contact acceleration detection.

A 3D Scanning System for Inverse Analysis of Moist Biological Samples: Design and Validation Using Tendon Fascicle Bundles.

In this article, we present the design and validation of a non-contact scanning system for the development of a three-dimensional (3D) model of moist biological samples. Due to the irregular shapes and low stiffness of soft tissue samples, the use of a non-contact, reliable geometry scanning system with good accuracy and repeatability is required. We propose a reliable 3D scanning system consisting of a blue light profile sensor, stationary and rotating frames with stepper motors, gears and a five-phase stepping motor unit, single-axis robot, control system, and replaceable sample grips, which once mounted onto the sample, are used for both scanning and mechanical tests. The proposed system was validated by comparison of the cross-sectional areas calculated based on 3D models, digital caliper, and vision-based methods. Validation was done on regularly-shaped samples, a wooden twig, as well as tendon fascicle bundles. The 3D profiles were used for the development of the 3D computational model of the sample, including surface concavities. Our system allowed for 3D model development of samples with a relative error of less than 1.2% and high repeatability in approximately three minutes. This was crucial for the extraction of the mechanical properties and subsequent inverse analysis, enabling the calibration of complex material models.

A Novel Analog Front End with Voltage-Dependent Input Impedance and Bandpass Amplification for Capacitive Biopotential Measurements.

This paper proposes a novel analogue front end (AFE) that has three features: voltage-dependent input impedance, bandpass amplification, and stray capacitance reduction. With a view to applying the AFE to capacitive biopotential measurements (CBMs), the three features were investigated separately in a schematic and mathematical manner. Capacitive electrocardiogram (cECG) or capacitive electromyogram (cEMG) measurements using the AFE were performed in low-humidity conditions (below 35% relative humidity) for a total of seven human subjects. Performance evaluation of the AFE revealed the following: (1) the proposed AFE in cECG measurement with 1.70-mm thick clothing reduced the baseline recovery time and root mean square voltage of respiratory interference in subjects with healthy-weight body mass index (BMI), and increased R-wave amplitude for overweight-BMI subjects; and (2) the proposed AFE in cEMG measurement of biceps brachii muscle yielded stable electromyographic waveforms without the marked DC component for all subjects and a significant (p < 0.01) increase in the signal-to-noise ratio. These results indicate that the proposed AFE can provide a feasible balance between sensitivity and stability in CBMs, and it could be a versatile replacement for the conventional voltage follower used in CBMs.

A Siamese Network-Based Non-Contact Measurement Method for Railway Catenary Uplift Trained in a Free Vibration Test.

The vibration of the catenary that is initiated by the passing pantograph has a direct influence on the pantograph-catenary contact performance. Monitoring the dynamic uplift of the catenary can help inspectors to evaluate the railway operation conditions and investigate the mechanism of pantograph-catenary interaction further. In this paper, a non-contact measurement method based on the deep leaning method is proposed to monitor the real-time vibration of the catenary. The field test for the catenary free vibration is designed to validate the method's performance. The measurement method is developed based on the fully convolutional Siamese neural network, and the contact wire is taken as the tracking target. To reduce the recognition errors caused by the changes in the shape and grayscale of the moving contact wire in images, the class-agnostic binary segmentation mask is adopted. A developed down-sampling block is used in the neural network to reduce the image feature loss, which effectively enhances the recognition effect for the catenary vibration under variable lighting conditions. To validate the performance of the proposed measurement method, a series of field tests of catenary free vibration were conducted under various lighting conditions and different excitations, and the recognition results were compared with traditional target tracking methods. The results show that the proposed method performs well for catenary vibration identification in the field test. Additionally, the uplift data extracted from the identified images agree with the numerical results, and also help to further investigate the wave propagation and damping characteristics in the catenary structure.

Body Dimension Measurements of Qinchuan Cattle with Transfer Learning from LiDAR Sensing.

For the time-consuming and stressful body measuring task of Qinchuan cattle and farmers, the demand for the automatic measurement of body dimensions has become more and more urgent. It is necessary to explore automatic measurements with deep learning to improve breeding efficiency and promote the development of industry. In this paper, a novel approach to measuring the body dimensions of live Qinchuan cattle with on transfer learning is proposed. Deep learning of the Kd-network was trained with classical three-dimensional (3D) point cloud datasets (PCD) of the ShapeNet datasets. After a series of processes of PCD sensed by the light detection and ranging (LiDAR) sensor, the cattle silhouettes could be extracted, which after augmentation could be applied as an input layer to the Kd-network. With the output of a convolutional layer of the trained deep model, the output layer of the deep model could be applied to pre-train the full connection network. The TrAdaBoost algorithm was employed to transfer the pre-trained convolutional layer and full connection of the deep model. To classify and recognize the PCD of the cattle silhouette, the average accuracy rate after training with transfer learning could reach up to 93.6%. On the basis of silhouette extraction, the candidate region of the feature surface shape could be extracted with mean curvature and Gaussian curvature. After the computation of the FPFH (fast point feature histogram) of the surface shape, the center of the feature surface could be recognized and the body dimensions of the cattle could finally be calculated. The experimental results showed that the comprehensive error of body dimensions was close to 2%, which could provide a feasible approach to the non-contact observations of the bodies of large physique livestock without any human intervention.

LED-CT Scan for pH Distribution on a Cross-Section of Cell Culture Medium.

In cell culture, the pH of the culture medium is one of the most important conditions. However, the culture medium may have non-uniform pH distribution due to activities of cells and changes in the environment. Although it is possible to measure the pH distribution with an existing pH meter using distributed electrodes, the method involves direct contact with the medium and would greatly increase the risk of contamination. Here in this paper, we propose a computed tomography (CT) scan for measuring pH distribution using the color change of phenol red with a light-emitting diode (LED) light source. Using the principle of CT scan, we can measure pH distribution without contacting culture medium, and thus, decrease the risk of contamination. We have developed the device with a LED, an array of photo receivers and a rotation mechanism. The system is firstly calibrated with different shapes of wooden objects that do not pass light, we succeeded in obtaining their 3D topographies. The system was also used for measuring a culture medium with two different pH values, it was possible to obtain a pH distribution that clearly shows the boundary.

Learning to See the Vibration: A Neural Network for Vibration Frequency Prediction.

Vibration measurement serves as the basis for various engineering practices such as natural frequency or resonant frequency estimation. As image acquisition devices become cheaper and faster, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In the conventional photogrammetry and optical methods of frequency measurement, vibration signals are first extracted before implementing the vibration frequency analysis algorithm. In this work, we demonstrate that frequency prediction can be achieved using a single feed-forward convolutional neural network. The proposed method is verified using a vibration signal generator and excitation system, and the result compared with that of an industrial contact vibrometer in a real application. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even in unfavorable field conditions.

PTZ Camera-Based Displacement Sensor System with Perspective Distortion Correction Unit for Early Detection of Building Destruction.

This paper presents a pan-tilt-zoom (PTZ) camera-based displacement measurement system, specially based on the perspective distortion correction technique for the early detection of building destruction. The proposed PTZ-based vision system rotates the camera to monitor the specific targets from various distances and controls the zoom level of the lens for a constant field of view (FOV). The proposed approach adopts perspective distortion correction to expand the measurable range in monitoring the displacement of the target structure. The implemented system successfully obtains the displacement information in structures, which is not easily accessible on the remote site. We manually measured the displacement acquired from markers which is attached on a sample of structures covering a wide geographic region. Our approach using a PTZ-based camera reduces the perspective distortion, so that the improved system could overcome limitations of previous works related to displacement measurement. Evaluation results show that a PTZ-based displacement sensor system with the proposed distortion correction unit is possibly a cost effective and easy-to-install solution for commercialization.

Visualization of Venous Compliance of Superficial Veins Using Non-Contact Plethysmography Based on Digital Red-Green-Blue Images.

We propose the visualization of venous compliance (VC) using a digital red-green-blue (RGB) camera. The new imaging method, which transforms RGB values into VC, combines VC evaluation with blood concentration estimation from the RGB values of each pixel. We evaluate a non-contact plethysmography (NCPG) system for VC based on comparisons with conventional strain gauge plethysmography (SPG). We conduct in vivo measurements using both systems and investigate their differences by evaluating the VC. The results show that the two methods measure different blood vessels and that errors caused by interstitial fluid accumulation are negligible for the NCPG system, whereas SPG is influenced by such errors. Additionally, we investigate the relationship between VC and physical activity using NCPG.

Reduction of Motion Artifacts and Improvement of R Peak Detecting Accuracy Using Adjacent Non-Intrusive ECG Sensors.

Non-intrusive electrocardiogram (ECG) monitoring has many advantages: easy to measure and apply in daily life. However, motion noise in the measured signal is the major problem of non-intrusive measurement. This paper proposes a method to reduce the noise and to detect the R peaks of ECG in a stable manner in a sitting arrangement using non-intrusive sensors. The method utilizes two capacitive ECG sensors (cECGs) to measure ECG, and another two cECGs located adjacent to the sensors for ECG are added to obtain the information on motion. Then, active noise cancellation technique and the motion information are used to reduce motion noise. To verify the proposed method, ECG was measured indoors and during driving, and the accuracy of the detected R peaks was compared. After applying the method, the sum of sensitivity and positive predictivity increased 8.39% on average and 26.26% maximally in the data. Based on the results, it was confirmed that the motion noise was reduced and that more reliable R peak positions could be obtained by the proposed method. The robustness of the new ECG measurement method will elicit benefits to various health care systems that require noninvasive heart rate or heart rate variability measurements.