A Framework in Calibration Process for Line Structured Light System Using Image Analysis

The detection accuracy of the light stripe centers is an important factor based on the structured light vision measurement, and the quality of the light stripe images is a prerequisite for accurately detecting the light stripe centers; this paper separately proposes image enhancement methods for linear and arc light stripe images. For linear light stripes, the image with better quality is captured and the gray-scale distribution of the normal section corresponding to the light stripe centers is used as a template for light stripe images with poor quality. The poor quality light stripe images are finally optimized by linear interpolation. For arc-shaped light stripes, this paper proposes a positioning method for light stripe centers on arc, and then the gray-scale distribution of the normal cross-section of corresponding center which is on a good quality light stripe image is used as templates to improve the poor quality light quality stripe images. In order to verify the effectiveness of the light stripe image enhancement algorithms, this paper respectively presents verification methods to linear light stripe and arc light stripe. Finally, the quality of the light stripe images could be improved by image enhancement algorithms through experiments.

Light plane calibration and accuracy analysis for multi-line structured light vision measurement system

In this paper, we propose a new visual inspection method that can analyze the exterior surface of rubber tires with the light stripe projection. Image sensing of the tire surface was implemented by setting the tire on the rotating table so that the normal pattern of the tire surface could be suppressed. The proposed method detects the exterior thin defect after removing three kinds of uneven circularities due to the real shape of tire, the eccentricity of rotating table and the inconsistency between two. Thus, in this research, we realized a visual inspection method by transforming three-dimensional shape of tire surface into two-dimensional image information through the analysis of light stripe images.

Alkanolamine solutions such as 2-amino-2-methyl-1-propanol (AMP) are widely used in chemical industries for the removal of acid gases such as CO2 and H2S. In this work, the density of CO2, AMP, water, and AMP solutions using the Goharshadi–Morsali–Abbaspour Equation of State “GMA EoS” in the extended 50-degree range of temperatures (313.06-362.65 K) and pressures (0.5-40 MPa) was calculated. The results showed that the GMA EoS can reproduce the density of these fluids within experimental errors throughout the liquid phase. The minimum and maximum absolute average deviations for the prediction of density for all studied fluids are -0.0082 and 5.2288, respectively. The values of statistical parameters between experimental and calculated thermodynamic properties such as isobaric expansion coefficient, isothermal compressibility, internal pressure, and solubility parameter of AMP show the ability of this equation of state in reproducing these properties.

Direct measurements of the enthalpy of vaporization for R290 + R1216 and R1234ze(E) binary mixtures by differential scanning calorimetry

New approach for evaluating form errors of engineering surfaces

To enhance the accuracy of lightweight CNN classification models in analyzing fish feeding behavior, this paper addresses the image quality issues caused by external environmental factors and lighting conditions, such as low contrast and uneven illumination, by proposing a Multi-step Image Pre-enhancement Strategy (MIPS). This strategy includes three critical steps: initially, images undergo a preliminary processing using the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm, effectively reducing the impact of water surface reflections and enhancing the visual effect of the images; secondly, the Multi-Metric-Driven Contrast Limited Adaptive Histogram Equalization (mdc) technique is applied to further improve image contrast, especially in areas of low contrast, by adjusting the local contrast levels to enhance the clarity of the image details; finally, Unsharp Masking (UM) technology is employed to sharpen the images, emphasizing their edges to increase the clarity of the image details, thereby significantly improving the overall image quality. Experimental results on a custom dataset have confirmed that this pre-enhancement strategy significantly boosts the accuracy of various CNN-based classification models, particularly for lightweight CNN models, and drastically reduces the time required for model training compared to the use of advanced ResNet models. This research provides an effective technical route for improving the accuracy and efficiency of an image-based analysis of fish feeding behavior in complex environments.

Line-structured light 3D measurement is often used for 3D contour reconstruction of objects in complex industrial environments, where light plane calibration is a key step. In this paper, we propose a calibration method for a line-structured optical system based on a hinge-connected double-checkerboards stereo target. First, the target is moved randomly in multiple positions at any angle within the camera measurement space. Then, by acquiring any one image of the target with line-structured light, the 3D coordinates of the light stripes feature points are solved with the help of the external parameter matrix of the target plane and the camera coordinate system. Finally, the coordinate point cloud is denoised and used to quadratically fit the light plane. Compared with the traditional line-structured measurement system, the proposed method can acquire two calibration images at once; thus, only one image of line-structured light is needed to complete the light plane calibration. There is no strict requirement for the target pinch angle and placement, which improve system calibration speed with high accuracy. The experimental results show that the maximum RMS error of this method is 0.075mm, and the operation is simpler and more effective to meet the technical requirements of industrial 3D measurement.

Solubility measurement of methane in triethylene glycol aqueous solutions (90,95,98 wt.% triethylene glycol) from 298 to 323 K and 0.17 to 2.5 MPa

We propose an alternative approach to calculate viscosity, based on the Rosenfeld scaling theory, which can be applied to pure components and their mixtures in a wide range of temperatures, pressures, and compositions. The Helmholtz scaling (A-scaling) applies the Chapman–Enskog relation, an Ansatz function, and the PCP-SAFT equation of state to predict the viscosity. Although it was implemented with the PCP-SAFT EoS, A-scaling has potential to be coupled with other EoS. Substances from n-alkanes, alcohols, aromatics, ethers, chlorofluorocarbons, carbon dioxide, and nitrogen were used to obtain five parameters to better correlate the experimental viscosity data. We tested the A-scaling performance of predicting the viscosity of 33 binary mixtures at different temperature and pressure conditions. For pure components, the minimum and maximum average absolute deviations (AADs) were 1.51 and 6.53%, respectively. Meanwhile, for mixtures, 0.70 and 23.95% were the lowest and highest values. By comparing this method with the successful and consolidated entropy scaling, it was found that AADs in most of the pure substances and mixtures were lower for A-scaling. Thus, since it is suitable for polar, nonpolar, and hydrogen-bonding substances, A-scaling covers many substances for industry applications, with no usage of adjustable parameters to predict viscosity for mixtures.

Curling is a common phenomenon observed in knitted fabrics. Curling poses many problems for fabric and clothing production, especially for digital devices, where it can affect the production quality and efficiency, while there is still no accurate method for evaluating it. This study established an evaluation method for the curling properties of knitted fabrics based on pixel statistics. Weft knitted fabric samples were created using a fabric cutter and a piece of paper was placed underneath the fabric. The fabric sample curls, whereas the paper represents a non-curling state. Subsequently, a scanner was used to scan the projections of the circular samples and paper to obtain digital images. The pixel amounts of the curled fabric and paper digital images were measured using the Photoshop 2020 software. The ratio between the fabric and paper projection pixels can be calculated by subtracting from 100% and converting the curling area ratio to the curling distance ratio. This can be used to represent the curling properties of knitted fabrics. The error between the theoretical and measured values is <0.1%, indicating a high level of accuracy. Ten knitted fabrics were validated for analysis. The sample with the maximum deviation between the left, middle, and right positions was 5.9%, whereas that with the minimum deviation was 0.3%. Subsequently, the fabrics were retested on the 7th and 14th days to assess reproducibility. The maximum deviation for each fabric on the 1st, 7th, and 14th days was 4.1%, and the minimum deviation was 0%. Therefore, it can be concluded that the method described in this study can accurately quantify the curling properties of knitted fabrics.

Key-cap flatness detection after assembly is one of the basic quality control (QC) indexes in computer keyboard manufacturing. A modified machine vision system based on linear structured light imaging for measuring the key-cap flatness is proposed for keyboard QC automation. After a brief introduction of the system design and principle, the pipeline of light stripe image processing, especially the removal of printed letter interference, is studied. First, the staggered reprojection of dense multiline fringes is presented using the pattern editability of the digital light processing projector to replace the conventional three-dimensional (3-D) sensor mechanical scanning and avoid the movement and cumulative error. Second, an adaptive direction operator based on a Hough transform voting is proposed. This operator is used for directional morphological filtering to remove letter noise and solve the issue of printed letter interference on the key-cap surface, thus improving the accuracy and stability of stripe centerline extraction. Finally, the nonlinear least square method is used to fit the 3-D surface of the key-cap and evaluate its flatness efficiently based on the discrete globally distributed 3-D point cloud data. The experimental result demonstrates that the proposed machine vision system can quickly detect keyboard key-cap flatness and shows superior performance to that of the previous work.

Calibration of a 3D vision system using pattern projection

A calibration method for line-structured light system by using sinusoidal fringes and homography matrix

3D measurement system based on divergent multi-line structured light projection, its accuracy analysis