Sub-pixel Edge Detection Research Articles

Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision

In-process measurement of machining precision is of great importance to advanced manufacturing, which is an essential technology to realize compensation machining. In terms of cost-effectiveness and repeatability of computer vision, it has become a trend to replace traditional manual measurement with computer vision measurement. In this paper, an in-process measurement method is proposed to improve precision and reduce the costs of machining precision. Firstly, a universal features model framework of machining parts is established to analyze the CAD model and give standard information on the machining features. Secondly, a window generator is proposed to adaptively crop the image of the machining part according to the size of features. Then, the automatic detection of the edges of machining features is performed based on regions of interest (ROIs) from the cropped image. Finally, the measurement of machining precision is realized through a Hough transform on the detected edges. To verify the effectiveness of the proposed method, a series of in-process measurement experiments were carried out on machined parts with various features and sheet metal parts, such as dimensional accuracy measurement tests, straightness measurement tests, and roundness measurement tests under the same part conditions. The best measurement accuracy of this method for dimensional accuracy, straightness, and roundness were 99%, 97%, and 96%, respectively. In comparison, precision measurement experiments were conducted under the same conditions using the Canny edge detection algorithm, the sub-pixel edge detection algorithm, and the Otsu–Canny edge detection algorithm. Experimental results show that the feature-model-based in-process measurement of machining precision using computer vision demonstrates superiority and effectiveness among various measurement methods.

Accurate measurement method for parallel edge spacing of sheet metal parts in aviation manufacturing

A new method, to our knowledge, is proposed to achieve high-precision measurement of parallel edge spacing for sheet metal parts in the complex industrial environment of aviation manufacturing. First, the sub-pixel edges of sheet metal parts are extracted by combining a what we believe to be a novel adaptive rolling bilateral filter and a sub-pixel edge detection algorithm based on the Canny–Steger algorithm. Then, the acquired edge data are denoised by using the clustering algorithm. Finally, a parallel line fitting algorithm, which combines an improved K-medoids algorithm with composite constraints on points and slopes, is proposed to calculate the parallel edge spacing. The results show that the method is robust to introducing noise in the edge data due to uneven illumination and various types of defects such as wear, scratches, and stains. The detection accuracy is high, with an average detection error of only 0.015 mm.

High-Precision Camera Calibration Method Based on Subpixel Edge Detection and Circularity Correction Compensation

High-Precision Camera Calibration Method Based on Subpixel Edge Detection and Circularity Correction Compensation

Video Motion Magnification and Subpixel Edge Detection-Based Full-Field Dynamic Displacement Measurement

Noncontact measurement techniques in structural dynamics field have progressed significantly in the past few decades. Vision-based measurement techniques are unique in that they have the ability to achieve full-field measurement and possess the typical advantages associated with noncontact measurement techniques. Recently, vision-based techniques have also been applied to streaming of videos for structural dynamic displacement measurement. The most recent trends in vision-based measurements include target tracing, digital image correlation, and target-less approaches. There are, however, some shortcomings of the vision-based techniques such as susceptibilities to image noise, prevailing light conditions, and limit in measurement resolution. To reduce these shortcomings, a method known as video motion magnification (MM) can be used to amplify small structural motions. Using the phase-based motion magnification (PBMM) and subpixel edge detection methods, the full-field dynamic displacements of the structure can be obtained. The deep convolutional long short-term memory (ConvLSTM) network is applied to aid in the selection of the frequency band for magnification in the PBMM algorithm. To achieve higher measurement accuracy, the displacement results with and without MM are combined with the finite impulse response (FIR) filter which can reduce the error caused by the PBMM procedure. In the tests, plastic optical fiber (POF) displacement sensors are introduced and used as reference measurements to compare the dynamic displacement results from the proposed vision-based method. Compared with the measured displacements with POF sensors, the proposed method offers high level of accuracy for full-field displacement measurement.

Visual measurement of the bearing diameter based on the homography matrix and partial area effect

A measurement method based on the homography matrix and partial area effect is studied to improve the precision of the visual measurement of the bearing size. Firstly, the camera is calibrated to obtain its distortion coefficient, and the image is corrected for distortion. Secondly, the relationship between the pixel plane and the bearing imaging plane is determined by solving the homography matrix with a chessboard. Finally, the sub-pixel edge contour of the bearing is obtained through the improved partial area effect, and the pixel coordinates are mapped to the world coordinates according to the homography matrix. The feature size is obtained by fitting the mapping points. Experimental results showed that the transformation of the homography matrix is more accurate than the pixel equivalent in the process of converting the pixel size to the actual size. The improved partial area effect sub-pixel edge detection results are better than the partial area effect and Zernike moment. The measurement error for the bearing outer diameter is less than 0.05 mm and is within 0.5 pixel.

Machine Vision Based Defect Detection Method for Electronic Component Solder Pads

<p>This paper proposes a machine vision based solder pad detection method to improve the detection accuracy and efficiency of PCB solder pad defects in electronic components due to missed detection and low detection efficiency. Firstly, preprocess the electronic component pad images collected by the visual system, then use threshold segmentation method to perform preliminary segmentation of the pad images. Then, the coarse segmented images are finely segmented using mean clustering method, and the fine segmented images are pixel edge extracted. Finally, the matrix subpixel edge detection method is used to improve the edge detection accuracy. Simulation experiments have shown that the proposed method can significantly improve the accuracy and speed of defect recognition.</p> <p> </p>

A Cost-Effective Method for Automatically Measuring Mechanical Parts Using Monocular Machine Vision.

Automatic measurements via image processing can accelerate measurements and provide comprehensive evaluations of mechanical parts. This paper presents a comprehensive approach to automating evaluations of planar dimensions in mechanical parts, providing significant advancements in terms of cost-effectiveness, accuracy, and repeatability. The methodology employed in this study utilizes a configuration comprising commonly available products in the industrial computer vision market, therefore enabling precise determinations of external contour specifications for mechanical components. Furthermore, it presents a functional prototype for making planar measurements by incorporating an improved subpixel edge-detection method, thus ensuring precise image-based measurements. The article highlights key concepts, describes the measurement procedures, and provides comparisons and traceability tests as a proof of concept for the system. The results show that this vision system did achieve suitable precision, with a mean error of 0.008 mm and a standard deviation of 0.0063 mm, when measuring gauge blocks of varying lengths at different heights. Moreover, when evaluating a circular sample, the system resulted in a maximum deviation of 0.013 mm, compared to an alternative calibrated measurement machine. In conclusion, the prototype validates the methods for planar dimension evaluations, highlighting the potential for enhancing manual measurements, while also maintaining accessibility. The presented system expands the possibilities of machine vision in manufacturing, especially in cases where the cost or agility of current systems is limited.

Real-Time Defect Detection for Metal Components: A Fusion of Enhanced Canny–Devernay and YOLOv6 Algorithms

Due to the presence of numerous surface defects, the inadequate contrast between defective and non-defective regions, and the resemblance between noise and subtle defects, edge detection poses a significant challenge in dimensional error detection, leading to increased dimensional measurement inaccuracies. These issues serve as major bottlenecks in the domain of automatic detection of high-precision metal parts. To address these challenges, this research proposes a combined approach involving the utilization of the YOLOv6 deep learning network in conjunction with metal lock body parts for the rapid and accurate detection of surface flaws in metal workpieces. Additionally, an enhanced Canny–Devernay sub-pixel edge detection algorithm is employed to determine the size of the lock core bead hole. The methodology is as follows: The data set for surface defect detection is acquired using the labeling software lableImg and subsequently utilized for training the YOLOv6 model to obtain the model weights. For size measurement, the region of interest (ROI) corresponding to the lock cylinder bead hole is first extracted. Subsequently, Gaussian filtering is applied to the ROI, followed by a sub-pixel edge detection using the improved Canny–Devernay algorithm. Finally, the edges are fitted using the least squares method to determine the radius of the fitted circle. The measured value is obtained through size conversion. Experimental detection involves employing the YOLOv6 method to identify surface defects in the lock body workpiece, resulting in an achieved mean Average Precision (mAP) value of 0.911. Furthermore, the size of the lock core bead hole is measured using an upgraded technique based on the Canny–Devernay sub-pixel edge detection, yielding an average inaccuracy of less than 0.03 mm. The findings of this research showcase the successful development of a practical method for applying machine vision in the realm of the automatic detection of metal parts. This achievement is accomplished through the exploration of identification methods and size-measuring techniques for common defects found in metal parts. Consequently, the study establishes a valuable framework for effectively utilizing machine vision in the field of metal parts inspection and defect detection.

Application of subpixel edge detection in quality control of double‐column metal parts

AbstractAt present, most of the parts of mechanical equipment are made of metal, so the accurate detection and extraction of important characteristic parameters of metal parts is the key to determine the quality of mechanical equipment. In order to accurately extract and measure the feature size of metal parts, this research first carried out image preprocessing. Threshold segmentation is performed, and the target traits are extracted from the image to obtain the ROI. Finally, the preprocessed image is extracted from the image by the sub‐pixel edge extraction technology to extract the feature size of the circle to be measured. The difference between the measured value of the characteristic dimension of the spring bearing seat and the gasket and the real value measured by the research method is within the range of 5 and 4 μm, respectively, and both of them meet the requirements of the characteristic dimension accuracy. When the feature size of the two is repeatedly measured, the variation range of the detection results of the spring bearing seat and the gasket is within 9 and 7 μm respectively, and the detection value is much smaller than the feature size tolerance. The detection accuracy of the method can reach 95.394%. The score was 96.029 and the AUC value was 0.93, both higher than other methods. The results show that the use of sub‐pixels on metal parts The edge extraction method is extremely accurate. The research method can effectively improve the detection accuracy of the feature size of metal parts, which is of great significance to the development of the entire metal parts processing industry.

Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking

Most research on computer vision (CV)-based vibration measurement is limited to the determination of discrete or coarse mode shapes of the structure. The continuous edge of the structure in images has rich optical features, and thus, by identifying and tracking the movement of the structure’s edge, it is possible to determine high-resolution full-field mode shapes of the structure without a preset target. The present study proposes a CV-based method of full-field mode shape identification using the subpixel edge detection and tracking techniques. Firstly, the Canny operator is applied on each frame of the structure vibration video to extract the pixel-level edges, and the improved Zernike orthogonal moment (ZOM) subpixel edge detection technique is adopted to relocate the precise structure edges. Then, all the detected edge points are tracked to obtain the full-field dense displacement time history that is subsequently used to determine the structure frequencies and compute full-field mode shapes by combining the covariance driven stochastic subspace identification (SSI-COV) with the hierarchical cluster analysis. Finally, the proposed method is verified on the aluminum cantilever beam in the laboratory and the Humen Bridge in the field. The results show that the proposed method is able to detect more precise structure edges and identify the full-field displacement and mode shapes of structures without the need for installing artificial targets on the structure in advance, which provides valuable information for the structural condition assessment, especially for structures with small-amplitude vibrations.

MTF Measurement by Slanted-Edge Method Based on Improved Zernike Moments.

The modulation transfer function (MTF) is an important parameter for performance evaluation of optical imaging systems in photogrammetry and remote sensing; the slanted-edge method is one of the main methods for measuring MTF. To solve the problem of inaccurate edge detection by traditional methods under the conditions of noise and blur, this paper proposes a new method of MTF measurement with a slanted-edge method based on improved Zernike moments, which firstly introduces the Otsu algorithm to automatically determine the Zernike moment threshold for sub-pixel edge detection to precisely locate the edge points, then obtains LSF through edge point projection, ESF sampling point acquisition, smoothing, fitting, taking ESF curve differential and Gaussian fitting, and finally, accurately obtaining MTF by LSF Fourier transform and modulo normalization. Based on simulation experiments and outdoor target experiments, the reliability of the proposed algorithm is verified by the deviations of slanted-edge angle and MTF measurement, and the tolerance degree of edge detection to noise and ambiguity are analyzed. The results show that compared with ISO 12233, OMNI-sine method, Hough transform method and LSD method, this algorithm has the highest edge detection accuracy, the maximum tolerance of noise and ambiguity, and also improves the accuracy of MTF measurement.

Research on the Real-Time Detection Method for Image Processing – Based Civil Structure Crack

Early discovery of civil structure crack facilitates timely treatment, such as repair, reinforcement and grouting. The image processing – based rapid and accurate detection method for civil structure crack brings important research value. In view of complicated construction site of civil project and complex and diversified civil structure, existing method cannot realize crack detection and geometric measurement accurately. Therefore, this article researches the real-time image processing-based detection method for civil structure crack. Bilateral filtering algorithm was used to finish smoothing processing. Crack edge of civil structure was extracted with interpolation calculation – based subpixel edge detection algorithm, to obtain more accurate crack edge position. Civil structure crack was detected based on end-to-end object detection network YOLO. The effectiveness of the model built was verified according to experimental results.

Research on the Measurement of Thermal Deformation of Tools on High-speed Machining Centers Based on Image Processing Technology

This paper focuses on the issues of tool thermal deformation during machine preheating，designing an image-processing-based solution for measuring these tool thermal deformation, to obtain the axial thermal error of the tool as a function of preheating time.This paper uses a high-speed camera to collect images of tool thermal deformation. Using MATLAB software, rough localization of images by Canny algorithm for edge extraction. Accurately locating tool edge outlines using a sub-pixel fitted edge detection method, that is, using the least squares method to fit a tool tip arc curve. From this, the thermal deformation during tool preheating is calculated. This study will serve as a basis for the compensation of thermal errors in machine tools.

An Improved Canny-Zernike Subpixel Detection Algorithm

This paper proposed a new subpixel detection method that detects subpixel edges directly, as opposed to the previous method, which requires crossing the entire image. It shows superior subpixel detection on the edge directly, which enhances subpixel edge detection speed significantly. In order to overcome the problem of noise reduction, this paper employs bilateral filtering. The method first performed coarse localization with the improved operator to determine the coordinates and gradient direction of the edge points. Then, the Zernike moment algorithm was used for subpixel repositioning of edge points. Finally, subpixel level edge positioning of the image is obtained. The detection algorithm is used to identify the edges of large-size workpieces, and the results reveal that the approach has superior positioning accuracy, noise immunity, and fast detection speed.

Laser machining and compensation truing of small concave-arc bronze-bonded diamond grinding wheels

A laser can be effectively used as a “dressing pen” to dress super-abrasive forming grinding wheels. However, laser truing and sharpening methods are in their primary stages. Problems caused by poor dressing persist in the absence of a theoretical analysis of trimming, and there is no effective compensation algorithm to correct actual and theoretical deviations. A pulsed fibre laser was used to dress a small concave arc-profiled 230# bronze-bonded diamond grinding wheel in this study. The Canny-based sub-pixel edge detection algorithm was improved to effectively enhance the detection of circular edges. Experiments were performed on coarse laser dressing of profiling concave-arc wheels, and the regularity of the radius, angle and depth of the arc was investigated. An error analysis model for the laser profiling of small concave-arc grinding wheels was developed and used to determine the factors causing error and that the concave-arc grinding wheel produces a minimum roundness error of 34 μm under the considered experimental dressing conditions. A novel error compensation method for trimming was developed, the horizontal approximation compensation method (HACM). Compensated trimming experiments were performed with effective error compensation. The final trimming dimensional error was approximately 11.2 μm, and the roundness error was approximately 22.8 μm. The main external factors causing these errors were laser thermal effects and the burrs detected on the graphite plate.

A novel method for measuring the length of hot large forgings based on machine vision system

In order to measure the length of hot large forgings, a novel method based on machine vision system is proposed. Firstly, according to the characteristics that the light strips in the images acquired by machine vision system are continuous and have peaks, the light strip is detected. Secondly, after calculating the sub-pixel edge points of each light strip using an improved sub-pixel edge detection algorithm, the sub-pixel edge points of each light strip are sorted according to the order of the abscissa from small to large. Three-dimensional (3D) points of each edge can be obtained separately by matching and 3D reconstruction. Finally, the 3D points of each edge are projected onto its fitting plane, and the projected points are fitted with a quintic polynomial curve. According to the curvature of the fitted curve, the edge point of the forging can be detected. The distance from the starting point to the edge point is the length of forging at the position of light strip edge. The length measurement experiment shows that the method can be used to measure the length of hot large forgings. The relative error of the length measurement system is 0.547%. The time for measuring the length of hot forging is 6.8 s.

Sub-pixel dimensional and vision measurement method of eccentricity for annular parts.

Eccentricity measurement of annular parts with millimeter scale and micrometer precision requirements is widely used in mechanical engineering applications. To realize accurate eccentricity measurement for large-scaled annular parts, a vision-based and sub-pixel dimensional measurement method is proposed. First, to facilitate the eccentricity measurement, an improved auto focus algorithm is introduced to provide better focused images of the measured parts. Then the traditional Canny operator is modified in gradient direction calculation and a double threshold process to locate the pixel edge more accurately. Next, a model-based sub-pixel edge detection method is studied to extract the sub-pixel edge coordinates. Finally, the eccentricity is calculated according to these sub-pixel edge coordinates. To guarantee measurement accuracy, the pixel equivalent and manual installation error of three degree of freedom (DOF) stages are calibrated, and the verification experiments indicate that the measurement error of the proposed method is better than 1.0 µm.

Subpixel edge detection algorithm based on improved Gaussian fitting and Canny operator

In image processing, image edge is often used as a basic feature in higher-level image processing. Edge detection technology is the basis of image processing technologies such as image measurement, image segmentation, image compression and pattern recognition. It is one of the important research topics in digital image processing. In this paper, we explore the image edge and pixel data, and propose an optimization model for sub-pixel edge extraction, image distortion correction and image edge segmentation. We preprocess the image with Gaussian filter, median filter and morphological close operation to reduce the impact of lighting environment and noise on the image. After edge detection with Canny operator, we use two-dimensional interpolation to Gaussian fit the edge points in the gradient direction to obtain the sub-pixel edge, and then search the pixel points to obtain the pixel order of the graphic outline and display it with different colors. Finally, we summarize the model, adjust the search range and extend it to the case of low definition of the original image. We can obtain high-precision contour edges of low pixels through technical means.

An adaptive sub-pixel edge detection method based on improved Zernike moment

An adaptive sub-pixel edge detection method based on improved Zernike moment

An adaptive sub-pixel edge detection method based on improved Zernike moment

An adaptive sub-pixel edge detection method based on improved Zernike moment