Steger Algorithm Research Articles

Research on Point Cloud Acquisition and Calibration of Deep Hole Inner Surfaces Based on Collimated Ring Laser Beams.

In this study, a ring light point cloud calibration technique based on collimated laser beams is developed, aiming to reduce errors caused by the position and attitude changes of traditional ring light measurement devices. This article details the generation mechanism of the ring beam and the principle of deep hole measurement. It introduces the collimated beam as a reference, building on traditional ring light measurement devices, to achieve the synchronous acquisition of the ring beam and collimated spot images by an industrial camera. The Steger algorithm is employed to accurately extract the coordinates of the point cloud contours of both the ring beam and the collimated spot. By analyzing the shape and position changes of the collimated spot contour, the spatial position and attitude of the measuring device are precisely determined. This technique is applied to the 3D reconstruction of the inner surface of deep holes, ensuring the accurate restoration of the spatial positional attitude of the ring beam by incorporating the spatial positional attitude parameters of the measuring device to precisely calibrate the cross-sectional point cloud coordinates. Experimental results with ring gauges and deep hole workpieces demonstrate that this technique effectively reduces the percentage of point cloud data outside the tolerance range, and improves the accuracy of the 3D reconstruction model by 6.287%, thereby verifying the accuracy and practicality of this technique.

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.

Extraction of laser stripe centerlines from translucent optical components using a multi-scale attention deep neural network

The precise extraction of laser stripe centerlines is critical for line-laser 3D scanning systems. However, conventional methods relying on threshold segmentation and morphological operations face significant challenges when confronted with pervasive optical phenomena, including specular reflection, scattering, and bleeding, which are commonly observed in translucent optical components. These methods typically require complex preprocessing procedures and often yield poor precision in centerline extraction. In this paper, we introduce a novel learning-based approach, complemented by a meticulously curated dataset, explicitly designed to address these challenges. Our proposed method leverages a multi-scale attention U-Net-like architecture, initially tasked with the segmentation of laser stripes from the complex background environment. Subsequently, it employs the Steger algorithm for the precise extraction of laser stripe centerlines. The experimental results, obtained by comprehensively evaluating real-world captured images, clearly demonstrate the effectiveness of our deep neural network combined with the Steger algorithm. This combined approach exhibits exceptional accuracy even when challenged by the interferences from specular reflection, scattering, and bleeding artifacts. Specifically, our method achieves a mean intersection over union (mIoU) of 84.71% for the laser stripe detection task, accompanied by a mean square error (MSE) of 10.371 pixels. Also, the average execution time for the centerline extraction task is notably efficient at 0.125 s.

Research Progress of Weld Tracking Image Processing Technology Based on Deep Learning Theory

Abstract In this paper, a convolutional neural network is used to localize the weld seam feature points with noise interference in complex welding environments. A priori frames are introduced into the feature point extraction network, combined with position prediction and confidence prediction, to improve the accuracy and anti-interference ability of the weld tracking system. To improve welding efficiency by utilizing the continuity of weld tracking, the weld tracking network is designed based on the twin structure. The weld detection network designs the first frame to locate the key position of the bevel and inputs into the weld tracking network as a template, and the weld tracking network completes the automatic tracking of the subsequent welds. At the same time, the network introduces a hybrid domain attention mechanism, which makes full use of the weld feature channel dependence and spatial location relationship and puts more attention near the inflection point of the weld laser line to ensure the accuracy of weld tracking. The research results show that the extraction error of weld seam feature points based on the convolutional neural network is within 17, which is much lower than that of the grayscale center of gravity method and Steger's algorithm. In the weld tracking experiments under the workpiece tilting state, the average value of the absolute error of the tracking trajectory in the X-axis direction is not more than 0.7 mm, and the maximum value is less than 1.15 mm. The absolute tracking error in the Z-axis direction is relatively low, with an average of 0.638 mm and a maximum of 1.573 mm. Therefore, the weld-tracking image processing technique proposed in this paper has strong anti-noise interference capabilities and high localization accuracy. And high accuracy in localization.

Method for three-dimensional reconstruction of dynamic stereo vision based on line structured light using global optimization

To overcome the limitation of the measurement range of static stereo vision, a three-dimensional (3-D) reconstruction method of dynamic stereo vision based on line structured light for metal surface is presented in this paper. The dynamic stereo vision is mainly composed of binocular cameras and a laser line generator, and moves along a horizontal guide rail. The intrinsic parameters of the cameras and the rotation and translation of the right camera relative to the left camera are previously obtained by Zhang’s method. To obtain the 3-D coordinates of the line structured light fringe in each position, the sub-pixel coordinates of the fringe center are extracted by Steger algorithm, and a joint calibration method between single camera and a laser line generator is proposed. The joint calibration method calculates the plane equation of the line structured light by using multiple checkerboard images with projected structured light line. To unify the 3-D coordinates of structured light fringe in each position to the reference coordinate system, the rotation and translation of the camera in each position relative to the reference position (called absolute pose) should be solved. The direct linear transformation method is firstly used to calculate the rough relative pose between adjacent frames, and principle of re-projection error minimization in object space is used to obtain the refined relative pose. Instead of using step-by-step transfer method, the absolute pose of the camera in each position is eventually calculated by using global optimization algorithm after the relative pose between all adjacent frames are obtained. Computer simulation and abundant experimental data validate the effectiveness of proposed method. The proposed 3-D reconstruction method using global optimization is respectively compared with step-by-step transfer method based on pixel re-projection error minimization and re-projection error minimization in object space. The experimental results reflect that our method is superior to step-by-step transfer method, and the 3-D reconstruction accuracy is improved by 28.05 % after global optimization.

Three-dimensional reconstruction of mobile binocular stereo vision based on push-broom line structured light for a workpiece surface

Stereo vision technology based on line structured light can effectively solve the problem of a three-dimensional (3D) reconstruction of a smooth surface. A method for 3D reconstruction of mobile binocular stereo vision based on push-broom line structured light for a workpiece surface is presented in this paper. The subpixel coordinates of the light strip centers of the line structured light are obtained by the Steger algorithm while the binocular module moves along the guide rail, and the polar constraint is used to achieve the matching of the extracted light strip centers. As a result, the 3D coordinates of the light strip centers in each location can be calculated because of the known interior and external parameters of the binocular module. To obtain the 3D point cloud data of the entire surface, a relative pose optimization method with respect to the initial frame is proposed, which accurately estimates the pose of the cameras in each location with respect to that in the initial location and unifies the 3D coordinates of the light strip centers in each location to the datum coordinates. The relative pose optimization method first estimates the rough values by using the direct linear transform method, and then iteratively calculates the refined solutions based on the principle of minimizing the re-projection errors. Simulation data and substantial experimental results validate the effectiveness of our method. Our method is compared to the direct linear transform method and the frame-by-frame transfer method, and the root mean square error (RMSE) of the distance from 3D point cloud to fitted plane is used to evaluate the 3D reconstruction accuracy. The repeatability experiment shows that the RMSE from our method is as low as 0.83 mm.

A Method of Binocular Laser 3-D Scanning Imaging for Reflective Workpieces

A deep learning line laser 3D measurement method based on feature fusion and attention mechanism is proposed to address the impact of reflective workpieces on the extraction of laser stripe centers. Firstly, a UNet segmentation model based on feature fusion and attention mechanism is established. The deep learning model can effectively solve the interference caused by reflection and can segment the overall distribution and bending characteristics of laser stripes. Secondly, the Steger algorithm is used to roughly extract the center of the laser stripe, and the contour polygon segmentation method is used to adaptively obtain the segmentation points of the laser stripe. Finally, polynomial fitting is performed based on segmented points to obtain a smoother laser stripe centerline. The experimental results show that the proposed method can effectively overcome the interference caused by reflective workpieces, and generate a smoother 3D model, and the measurement repeatability error is less than 0.37mm, and the relative error is less than 0.03mm.

Road extraction from low-cost GNSS-device dense trajectories

ABSTRACT This paper proposes a method for road centerline extraction from dense Global Navigation Satellite System (GNSS) trajectories, collected by using low-cost GNSS-devices, i.e. smartphones. The proposed method basically consists in generating a frequency image by tracking the GNSS trajectories and then by applying the Steger line detector to the generated image to extract the road centerlines. The main motivation of using the Steger algorithm is its capability to detect lines with sub-pixel accuracy. To evaluate the obtained results, reference road centerlines are manually extracted from a georeferenced orthomosaic. The experiments performed demonstrate the high potential of applying the Steger line detector to frequency images, generated by using dense GPS (Global Positioning System) trajectories. The completeness and correctness values for the accomplished experiments were 98% and 99%, respectively. Additionally, the RMSE (Root Mean Square Error) ranged from 0.63 m to 2.39 m, or approximately 1/16 to 1/4 of the expected accuracy (about 10 m) of a point determined by the Single-Point Positioning (SPP) method, which is the GNSS positioning method usually employed by smartphones.

Online measurement system of slab front-end bending in hot rough rolling based on line structured light vision

Front-end bending is one of the most significant defects of slabs in hot rough rolling. To address the lack of effective methods to detect slab front-end bending in hot rolling, a measurement system based on line structured light vision is proposed in this paper. Line structured light lasers and area-scan cameras are arranged on the supports on both the exit and entrance of a roughing mill. First, the calibration of the measurement system is completed by camera calibration and the random sampling consistency algorithm. Second, multiple regions of interest gray thresholds and the Steger algorithm are used to realize laser centerline extraction on the surface of a large-scale and high-temperature slab. Last, the actual value of the slab front-end bending is obtained and calculated through coordinate transformation. The proposed measurement system can detect slab front-end bending in real time and the relative mean deviation is 0.93%.

Automated Measurement of Geometric Features in Curvilinear Structures Exploiting Steger's Algorithm.

Accurately assessing the geometric features of curvilinear structures on images is of paramount importance in many vision-based measurement systems targeting technological fields such as quality control, defect analysis, biomedical, aerial, and satellite imaging. This paper aims at laying the basis for the development of fully automated vision-based measurement systems targeting the measurement of elements that can be treated as curvilinear structures in the resulting image, such as cracks in concrete elements. In particular, the goal is to overcome the limitation of exploiting the well-known Steger's ridge detection algorithm in these applications because of the manual identification of the input parameters characterizing the algorithm, which are preventing its extensive use in the measurement field. This paper proposes an approach to make the selection phase of these input parameters fully automated. The metrological performance of the proposed approach is discussed. The method is demonstrated on both synthesized and experimental data.

High-Accuracy Spectral Measurement of Stimulated-Brillouin-Scattering Lidar Based on Hessian Matrix and Steger Algorithm

The measurement accuracy of Brillouin scattering spectra is crucial for ocean remote sensing by Brillouin scattering lidar. Due to the limited resolution of ICCD cameras, the traditional processing methods remain at the pixel or partial sub-pixel level, which cannot meet the requirements of high-performance lidar. In this paper, to extract the frequency shift with high precision from stimulated Brillouin scattering (SBS) lidar, a novel spectral processing method with sub-pixel recognition accuracy is proposed based on the Hessian matrix and Steger algorithm combined with the least square fitting method. Firstly, the Hessian matrix and Frangi filter are used for signal denoising. Then, the center points of SBS spectra at the sub-pixel level are extracted using the Steger algorithm and are connected and classified according to the signal type. On that basis, the frequency shifts of Brillouin scattering are calculated by using the center and radii of interference spectra after through fitting by the least squares method. Finally, the water temperatures are inverted by using the frequency shifts of Brillouin scattering. The results show that the processing method proposed in this paper can accurately calculate the frequency shift of Brillouin scattering. The measured errors of frequency shift are generally at an order of MHz, and the inversion accuracy of water temperature can be as low as 0.14 °C. This work is essential to the application for remote sensing the seawater parameters by using the Brillouin lidar technique.

Realizing quality inspection in magnetic pulse crimping manufacturing: Using Steger algorithm and self-attention

Magnetic pulse crimping (MPC) is a high-speed forming using magnetic force to derive metal deformation, which has advantages of controllability and excellent formability. However, due to similar joint appearance and unobservable connection area, evaluating joint quality nondestructively is difficult. This paper presents a quality inspection using the similarity between typical joints (templates) and tests to solve the difficulty. Templates corresponded to joints with deformations ranging from insufficient to excessive (categories 0–4), and the intermediate was qualified. Deformed shapes of the joints on coaxial sections were analytical features to make the most of the correlation between shape and quality. Detailed analysis on quality-related shape avoids unobservable areas and explores subtle differences. Shapes were extracted from the structure light image using the Steger-based algorithm. Self-attention network, a machine learning algorithm, learned the shapes of typical joints to form a comparator. The comparator was used to determine which template shape (types 0–4) the different shapes of the test joint were similar to. The test joint category depended on which type has the maximum ratio in shape prediction. Steger extracts shapes without the discrete spots effect, abnormal and missing values. The comparator accuracy is 0.9701, 0.9841 and 0.9881 for training, validation, and testing. The F1 of the qualified category is 0.98. The untrained joints were used as validation. The maximum ratios are 0.878, 0.842, 0.764, 0.469, and 0.997, whose predicted categories coincide with the real ones. This proves that it is feasible to design inspection based on process characteristics.

A teaching-free welding position guidance method for fillet weld based on laser vision sensing and EGM technology

Position guidance plays an essential role in guaranteeing welding quality. However, the current position guidance methods usually rely on the teaching trajectory or a similarly designed weld path. The innovation of this manuscript is to establish a teaching-free position guidance method combining the position detected by laser vision with the data bidirectionally sent by externally guided motion (EGM) at a high rate. A feature point extraction method based on Steger algorithm and slope determination is proposed. A dense zig-zag path is generated according to the position guidance based on EGM. Meanwhile, the position guidance experiments for fillet weld (horizontal deflection angle α = 9.2°, 13.3° and vertical deflection angle β = 5.5°, 9.2°) are finished. The mean absolute error (MAE) of position guidance in the Y-axis and Z-axis can be controlled in 0.21 and 0.13 mm, respectively. Moreover, the robot welding experiments for fillet weld in three different horizontal and vertical deflection angles (α = 30°, β = 25°, α = 30° and β = 25°) show that this method can obtain good weld surface morphology and metallographic morphology, and have a potential application value in industrial automation.

A Hybrid Algorithm for the Laser Stripe Centreline Extraction

Centreline extraction is one of the critical processes of structured light systems that play an increasingly important role in industry 4.0. This paper proposes a novel algorithm to extract the centreline by combining an improved skeleton thinning algorithm to extract the initial centreline and an improved grey centroid algorithm to establish the final centreline. It consists of several steps: Firstly, an image difference operation and a thresholding operation are undertaken, and the Canny edge detection algorithm is applied to locate the approximate position of the laser stripe to reduce the image matrix calculation cost; Secondly, an initial centreline is extracted via the use of an image morphology operation and a skeleton thinning algorithm; Finally, a high-power grey centroid method is employed to acquire the centreline at the sub-pixel level. Experiments together with a comparison with other mainstream methods is conducted to test the validity and practicability of the proposed hybrid algorithm. The experimental results show that the speed of the proposed algorithm is 50% faster than the Steger algorithm, and the root-mean-square error is 40% lower than the grey centroid method.

REAL TIME MEASUREMENT AND ANALYSIS OF FULL SURFACE CRACKING CHARACTERISTICS OF CONCRETE BASED ON PRINCIPAL STRAIN FIELD

The measurement of the crack propagation process is significant to reveal the failure mechanism and evaluate the mechanical properties of concrete structure. This paper presents a method of crack location and width measurement based on the deformation field of concrete surface. The high-resolution deformation field of the concrete specimens is obtained by using multi-camera digital image correlation method at first. It is found that the virtual principal strain field around the crack obviously differs from that in uncracked zone because of the gradient of displacement field caused by crack, and the principal strain field can be regarded as approximately Gaussian distributed in the normal direction of the crack. A new method of crack location based on the principle strain field is proposed based on the Steger algorithm which is widely applied in laser stripe center extracting, and the difference of in-plane displacement vectors on the normal direction between two sides of the crack is obtained. The projection along the normal direction is taken as the width of mode I crack, while the projection along the normal vertical direction is taken as the width of mode II crack. The experiment of simulating crack propagation is performed by using a high precision translation table to verify the measurement accuracy of crack width measurent. The results of the experiments show that the measurement error of crack width is between 0.010 pixel and 0.017 pixel, which is consistent with the theoretical prediction. The standard deviation is between 0.006 pixel and 0.008 pixel, which illustrates that the measurement accuracy of the proposed method is high. The accuracy of the proposed method is better than that of image-based crack measurement method at the same image resolution. The method proposed can automatically measure crack propagation in real time, which provides a reliable and accurate method for the concrete experiment.

The Centerline Extraction Algorithm of Weld Line Structured Light Stripe Based on Pyramid Scene Parsing Network

Based on the good feature learning ability of the pyramid scene parsing network, a method for extracting the centerline of structured light stripes of weld lines based on the pyramid scene parsing network and Steger algorithm is proposed. This method avoids the traditional complex weld image preprocessing technology, and simplifies the operation steps of extracting the centerline of the structured light stripe of the weld image line. In this paper, the pyramid scene parsing network is used to predict the pixels containing weld feature information. Through the pyramid pooling module, the local and global context feature information is fused to supplement the feature information of the weld edge, and then the Steger algorithm is used to extract the weld feature centerline. The results show that the method in this paper can accurately extract the centerline position of the structured light stripe of the weld line under the interference of reflection, and the average value reaches 86.8% on the accuracy evaluation index mean intersection over union, the 18.93 pixels on the weld extraction accuracy index root mean square error, and the average time of extracting the center line of structured light stripe of weld line is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.188s$ </tex-math></inline-formula> .

Autofeeding System for Assembling the CBCs on Automobile Engine Based on 3-D Vision Guidance

Assembling crankshaft bearing caps (CBCs) on automobile engine is a vital and complicated task that manual work is usually inefficient and become more prone to error. In this article, an autofeeding system with vision and line laser combined with an industrial object location and classification method is proposed for assembling the CBCs. First, an improved fully convolution one-stage network is used to obtain 2-D localization and detect the placement direction and order of a group of CBCs in the CBCs' image. Based on the 2-D location information, the line laser projects the laser on the surface of the CBCs at two different positions, and the images are captured accordingly. Then, an improved Steger algorithm is proposed to extract the centerline of the laser light bar, and the two laser images are merged into one laser image to calculate the height and pose information of the CBCs. The experimental results demonstrate that our method has achieved high real-time performance, accuracy, and robustness. After comparison and application in the factory, it is illustrated that the proposed autofeeding system improves the productivity and saving manpower.

Vision-Based Position Deviation Measurement of Rail Corrugation Chord Measuring Points Under Bi-Linear Laser Assistance

During the dynamic measurement of chord-based rail corrugation, with the interferences of curve rail and train snaking movement, it is hard to ensure that the measuring points on the rail surface can trace the railhead centerline well the whole time. This introduces the position deviations to the measurement system, and leads to the measurement results cannot reflect the true rail irregularity state. To solve the problem, a position deviation measurement method based on the assistance of bi-linear laser is proposed in this paper. First, by means of three verifications composed of color, dimension and position, the laser stripe regions on railhead are located from original image effectively. Then, by suppressing stripe highlight texture and modifying the traditional Steger algorithm with flexible-widths Gaussian template, we extract the laser stripe centerline accurately, and depict the railhead boundary fast. Last, by locating three chord-based measuring points and calculating the distances from them to the railhead centerline, we obtain the position deviations of each point. The efficiency and superiority of proposed method are verified by vast experiments. The results show that the accuracy of the position deviation of the chord-based measuring points is limited within 0.4mm.

Linear object extraction based on hydrodynamics, fractional differential and Steger algorithm

Linear object extraction based on hydrodynamics, fractional differential and Steger algorithm

An adaptive feature extraction algorithm for multiple typical seam tracking based on vision sensor in robotic arc welding

Intelligent robotic welding is an indispensable part of modern welding manufacturing, and vision-based seam tracking is one of the key technologies to realize intelligent welding. However, the adaptability and robustness of most image processing algorithms are deficient during welding practice. To address this problem, an adaptive feature extraction algorithm based on laser vision sensor is proposed. According to laser stripe images, typical welding seams are classified into continuous and discontinuous welding seams. A Faster R-CNN model is trained to identify welding seam type and locate laser stripe ROI automatically. Before welding, initial welding point is determined through point cloud processing to realize welding guidance. During seam tracking process, the seam edges are achieved by a two-step extraction algorithm, and the laser stripe is detected by Steger algorithm. Based on the characteristics of two kinds of welding seams, the corresponding seam center extraction algorithms are designed. And a prior model is proposed to ensure the stability of the algorithms. Test results prove that the algorithm has good adaptability for multiple typical welding seams and can maintain satisfying robustness and precision even under complex working conditions.