Laser Stripe Center Research Articles

Method of 3D reconstruction of underwater concrete by laser line scanning

High-precision 3D (three-dimensional) reconstruction of underwater concrete is helpful for the detection and analysis of underwater concrete diseases. A prevalent method involves mounting optical cameras in waterproof housings to enable the 3D reconstruction of underwater concrete. Achieving millimetric accuracy in underwater 3D reconstruction poses a significant challenge in current research. The refraction of light on surfaces of different media and its reflection underwater complicate the process. In this paper, a 3D reconstruction method of underwater concrete based on laser line scanning is proposed. Firstly, the underwater imaging model is established by the ray tracing method, and the refraction conversion matrix is derived. Then, based on this matrix, the conversion relationship between the measured 3D size and the real 3D size of the underwater object is obtained. Finally, image restoration technology is introduced in the process of laser stripe extraction to eliminate the influence of reflected light on the extraction of laser stripe center, to improve the reconstruction accuracy. We used the independently developed laser line 3D scanning equipment to carry out experimental analysis. The results showed that the error rate caused by direct reconstruction of underwater objects was 9.75 %. Additionally, the error rate obtained from 3D reconstruction of underwater objects using the conversion model was 2.89 %. This study provides technical support for the 3D detection of underwater concrete.

3D measurement of precise part in complex circumstance using line structured light and improved U-Net

Abstract Line structured light scanning is extensively utilized for the 3D measurement of precise metal parts, but the curvature surfaces and specific materials of these parts generate specular reflection, making it challenging to accurately extract the center of the laser stripe in complex circumstances. Therefore, the primary challenges are the noise separation in the laser stripe image and the extraction of the laser stripe center under complex circumstances. To solve the above problems, an improved U-Net semantic segmentation algorithm is proposed by adding an attention mechanism and modifying skip connections to the classical U-Net network structure for accurate laser stripe segmentation. Secondly, the dual smoothing method of mean smoothing and Savitzky–Golay smoothing is combined with the Hessian matrix algorithm to complete the subpixel extraction of the center point of the laser stripe. Finally, taking the blade and shaft part as the measurement objects, the experimental results demonstrate that the method can obtain more complete, smoother, and denser results than the traditional method under highly reflective surfaces, vital interference spots, and strong ambient light. The proposed method is used for the 3D measurement of the shaft parts, and the diameter measurement maximum error is 0.029 mm, verifying the feasibility of the proposed method.

Underwater Structured Light Stripe Center Extraction with Normalized Grayscale Gravity Method.

The non-uniform reflectance characteristics of object surfaces and underwater environment disturbances during underwater laser measurements can have a great impact on laser stripe center extraction. Therefore, we propose a normalized grayscale gravity method to address this problem. First, we build an underwater structured light dataset for different illuminations, turbidity levels, and reflective surfaces of the underwater object and compare several state-of-the-art semantic segmentation models, including Deeplabv3, Deeplabv3plus, MobilenetV3, Pspnet, and FCNnet. Based on our comparison, we recommend PSPnet for the specific task of underwater structured light stripe segmentation. Second, in order to accurately extract the centerline of the extracted light stripe, the gray level values are normalized to eliminate the influence of noise and light stripe edge information on the centroids, and the weights of the cross-sectional extremes are increased to increase the function convergence for better robustness. Finally, the subpixel-structured light center points of the image are obtained by bilinear interpolation to improve the image resolution and extraction accuracy. The experimental results show that the proposed method can effectively eliminate noise interference while exhibiting good robustness and self-adaptability.

Laser stripe center extraction method base on Hessian matrix improved by stripe width precise calculation

The line laser measurement technology based on triangulation is widely used in the field of complex surface topography measurement. The key technology of line laser measurement is laser stripe center extraction. For high reflective surfaces, due to the low surface roughness, some areas of the laser stripe image are overexposed, resulting in uneven stripe width distribution, asymmetric light intensity distribution of the stripe cross section, and difficulty in extracting the center of the stripe. In this paper, Laser stripe center extraction method based on Hessian matrix method improved by stripe width is proposed. Firstly, the laser stripe image is preprocessed, the influence of noise in the image is alleviated by image filtering and adaptive threshold segmentation, and the region of interest of the image is extracted to reduce the amount of subsequent calculation. The laser stripe width is calculated based on BPNN and divided into different width intervals. The Gaussian kernel is calculated according to the light stripe width and the Hessian matrix is constructed. The Hessian matrix method is used to roughly extract the center coordinates of the light stripe. Based on the continuity of the light stripe, the center coordinates of the light stripe are weighted and corrected to realize the smooth optimization of the center coordinates of the light stripe. The experimental results show that the proposed light stripe center extraction method is superior to the traditional method under different surface roughness and exposure time conditions, which can effectively improve the measurement accuracy of the sensor.

LaserNet: a method of laser stripe center extraction under non-ideal conditions.

The extraction of the center of a laser stripe is a key step in line-structure measurement, where noise interference and changes in the surface color of an object are the main factors affecting extraction accuracy. To obtain sub-pixel level center coordinates under such non-ideal conditions, we propose LaserNet, a novel deep learning-based algorithm, to the best of our knowledge, which consists of a laser region detection sub-network and a laser position optimization sub-network. The laser region detection sub-network is used to determine potential stripe regions, and the laser position optimization sub-network uses the local image of these regions to obtain the accurate center position of the laser stripe. The experimental results show that LaserNet can eliminate noise interference, handle color changes, and give accurate results under non-ideal conditions. The three-dimensional reconstruction experiments further demonstrate the effectiveness of the proposed method.

Calibration of a circular structured light measurement system based on a multi-diameter calibrator

To solve the measurement problems of the inner shape of large-scale workpieces, such as excavators with movable arms, this paper proposes an inner shape measurement method based on circular structured light. First, the center of the circular laser stripe is extracted based on a modified Steger method. Second, a multi-diameter concentric calibration gauge design is used to calibrate the optical plane of the proposed system and address the problem of the optical plane deviation and motion error in the 1D linear stage of the traditional calibration process. Finally, the experiments are conducted. The corresponding experimental results demonstrate that the proposed measurement method facilitates a fast calibration of the camera and an efficient extraction of the center of the light stripe, resulting in a relative error of less than 0.012%. The measurement method can be further applied to deep-hole workpieces, such as steel tubes and natural gas tube lines, among others.

A real-time laser stripe center extraction method for line-structured light system based on FPGA

A real-time laser stripe center extraction method for line-structured light system based on FPGA

Laser Curve Extraction of Wheelset Based on Deep Learning Skeleton Extraction Network.

In this paper, a new algorithm for extracting the laser fringe center is proposed. Based on a deep learning skeleton extraction network, the laser stripe center can be extracted quickly and accurately. Skeleton extraction is the process of reducing the shape image to its approximate central axis representation while maintaining the image’s topological and geometric shape. Skeleton extraction is an important step in topological and geometric shape analysis. According to the characteristics of the wheelset laser curve dataset, a new skeleton extraction network, a hierarchical skeleton network (LuoNet), is proposed. The proposed architecture has three levels of the encoder–decoder network, and YE Module interconnection is designed between each level of the encoder and decoder network. In the wheelset laser curve dataset, the F1_score can reach 0.714. Compared with the traditional laser curve center extraction algorithm, the proposed LuoNet algorithm has the advantages of short running time, high accuracy, and stable extraction results.

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.

Robust laser stripe extraction for 3D measurement of complex objects

Laser stripe center extraction is a critical step for a structured-light system, which plays an important role in industrial fields. However, existing center extraction methods are mainly for simple objects, where the stripe contours are relatively clear and no fake stripe areas caused by surface texture or surface reflection disturb the extraction of the real stripe area. To develop center extraction methods for complex objects, this paper proposed a new laser stripe extraction method based on the features of the laser stripe. After extracting seed points of the laser stripe using an extreme value method and connected component analysis, we can determine the stripe directions from the seed points and iteratively grow the centerline of the laser stripe. Finally, a gray-gravity method is used to extract the accurate center points of the laser stripe based on the obtained centerline. Experiments on 2D image laser stripe extraction and real 3D reconstruction application verified the robustness and accuracy of the proposed method.

Robust Analysis and Laser Stripe Center Extraction for Rail Images

In this paper, a novel method for the effective extraction of the light stripes in rail images is proposed. First, a preprocessing procedure that includes self-adaptive threshold segmentation and brightness enhancement is adopted to improve the quality of the rail image. Secondly, center of mass is utilized to detect the center point of each row of the image. Then, to speed up the procedure of centerline optimization, the detected center-points are segmented into several parts based on the geometry of the rail profile. Finally, piecewise fitting is adopted to obtain a smooth and robust centerline. The performance of this method is analyzed in detail, and experimental results show that the proposed method works well for rail images.

Research on laser stripe characteristics and center extraction algorithm for desktop laser scanner

This study presents laser stripe center extraction algorithm for desktop-level 3D laser scanners. The laser stripe center extraction accuracy is an important factor affecting 3D scanning result. Desktop-level devices should have adaptability of a wide range of scanning objects. In this paper, laser stripe energy distribution characteristics with different laser stripe width, ambient light, materials and colors are obtained by experiments. Experiment results show that waveforms of bright spot, low brightness stripe and stripe with large width are complex or easily disturbed, so the center extraction algorithm of them are studied. The extraction effects of extremum method, gradient method and gray centroid method under different conditions are compared. Based on traditional grayscale value, a weighted grayscale value is proposed to extract laser stripe center. Standard deviations of extracted pixel position and fitting pixel position are calculated by different method with different weighted grayscale value. For different conditions, especially for different ambient light intensity, weight matrix plays an important role to extraction result.

3D surface reconstruction of small height object based on thin structured light scanning

Aiming at the problem of 3D surface reconstruction of small height objects, a method based on the scanning principle with thin structured light is proposed. The laser fringe is produced by a laser light source. It scans the surface of small height object under the control of a precise motion control system, and is integrated with a stereo light microscope and two cameras to form a complete structured light profilometry system. This method is very suitable for 3D surface reconstruction of small height objects. In order to deal with the phenomenon of "cracking" and local uneven brightness in microscope fringe image, the fringe image sequence is captured under different camera exposure parameters, and the quality of fringe image is improved by image fusion. The location of pixels on the center line of laser stripes is detected by the Loess local second-order fitting method, and the positions of these pixels are smoothed and predicted by using the Lowess local linear fitting method. The deformation of the laser stripe center curve is calculated by constructing a baseline, and the 3D surface reconstruction of the object with small height is realized in the image space. Taking the small characters on the surface of Chinese coins and human hair as test samples, their 3D surfaces are constructed by using the method proposed in this paper. The reconstructed 3D surface shapes are highly consistent with the real 3D surface shapes of objects.

Internal Propulsion Algorithm for Extracting Center of Line Laser Stripe

Internal Propulsion Algorithm for Extracting Center of Line Laser Stripe

Extraction of laser stripe center on translucent glue strip surface

Extraction of laser stripe center on translucent glue strip surface

A Robust Laser Stripe Extraction Method for Structured-Light Vision Sensing.

Environmental sensing is a key technology for the development of unmanned cars, drones and robots. Many vision sensors cannot work normally in an environment with insufficient light, and the cost of using multiline LiDAR is relatively high. In this paper, a novel and inexpensive visual navigation sensor based on structured-light vision is proposed for environment sensing. The main research contents of this project include: First, we propose a laser-stripe-detection neural network (LSDNN) that can eliminate the interference of reflective noise and haze noise and realize the highly robust extraction of laser stripes region. Then we use a gray-gravity approach to extract the center of laser stripe and used structured-light model to reconstruct the point clouds of laser center. Then, we design a single-line structured-light sensor, select the optimal parameters for it and build a car–platform for experimental evaluation. This approach was shown to be effective in our experiments and the experimental results show that this method is more accurate and robust in complex environment.

A novel high precise laser 3D profile scanning method with flexible calibration

To improve the accuracy of laser-contour profile devices, we propose a 3D laser scanning system with high accuracy that includes camera and laser plane calibration, and laser-stripe center extraction. First, a calibration device was designed and manufactured, from which the fiducial points can be easily and accurately detected to calibrate the laser plane. Then through the perspective invariance and the projective invariant cross-ratio properties, a flexible and accurate calibration algorithm is presented. A polynomial fitting method is employed to overcome the discontinuity and thickness of the laser stripe center. Finally, a cubic spline interpolation is utilized to restore the continuous gray value of the transitional regions of the light stripe. Experimental results with different test pieces demonstrate that the minimum geometric accuracy is 18µm and the repeatability is less than ± 3µm, which indicates that the proposed method is feasible to be used in industrial high precision measurement.

Sub-pixel extraction of laser stripe in complex background

The complex background and laser stripe noise affect laser stripe extraction. Adaptive double threshold segmentation method and the improved gray weight model are proposed in this study. First, the characteristics of the laser stripe and the source of noise in the image are investigated. Bilateral filter is applied to remove the noise of images. Subsequently, the gray histogram of laser image and the double threshold are computed. By sub-regional processing, initial stripe center and stripe width of binary images are obtained. Finally, the sub-pixel center of the laser strip is extracted by the proposed model. The double threshold segmentation method and the improved gray weight model are compared with the traditional algorithms. The results show that the double threshold method is more accuracy in extracting the laser stripe region than the extreme value method and the Otsu method. Comparing with the residual value of sub-pixel center, the improved gray weight model (0.23) has better results than the gray-gravity method (0.71), the extreme value method (0.86), and the Gaussian fitting method (0.86). The algorithms proposed in this study avoid the impacts of complex background and laser stripe noise, increase the accuracy of the laser stripe center positioning and extract the stripe center extraction fast and accurately in complex backgrounds.

基于海森矩阵与区域增长的激光条纹中心提取

基于海森矩阵与区域增长的激光条纹中心提取

行车环境下钢轨轮廓激光条纹中心的提取方法

行车环境下钢轨轮廓激光条纹中心的提取方法