Center Extraction Method Research Articles

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

A rigorous fastener inspection approach for high-speed railway from structured light sensors

Rail fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose fasteners. Firstly, precise and extremely dense point cloud of fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. The overall precision of the decision tree classifier is over 99.8% and the root-mean-square error of looseness check is 0.15 mm, demonstrating a reliable and effective solution for high-speed railway fastener maintenance.

Laser stripe extraction method in industrial environments utilizing self-adaptive convolution technique.

A line-structured laser scanner is widely applied for 3D reconstruction in industrial environments with ubiquitous various luminance, complicated background, diverse objects, and instable lasers. These elements will show up as noise in the obtained laser stripe images. Therefore, the basic and key point for a line-structured laser scanner is to accurately extract the laser stripe from noise. This paper proposes an effective laser stripe extraction procedure with two steps. First, a novel laser stripe center extraction method based on the geometry information and correlation in the laser stripe, is designed to significantly eliminate noise and accurately extract the laser stripe centers. In addition, the robustness, speed, and accuracy of this method are respectively analyzed in detail. Second, piecewise fitting is adopted to acquire a smooth and continuous laser stripe centerline. In order to select the optimal fitting method, the characteristics of two spline methods, Akima spline and cubic Hermite spline, are deeply analyzed and compared. Finally, an experiment is carried out by using a rough metal step and a line-structured laser scanning system. The experiment results demonstrate that the proposed self-adaptive convolution-mass method can significantly eliminate noise in industrial environments. In addition, the cubic Hermite spline is a better choice for 3D reconstruction, rather than the Akima spline.

基于各向异性高斯曲面拟合的星点质心提取算法

基于各向异性高斯曲面拟合的星点质心提取算法

Compressive sensing-based algorithm for one-dimensional scattering center extraction

This article proposes a one-dimensional scattering center extraction (SCE) method that includes mainly two steps: coarse estimation of scattering centers using the iteratively reweighted least square (IRLS) coupled with a peak-finding algorithm and fine estimation of scattering centers using the orthogonal matching pursuit (OMP) procedure from the adaptively sampled Fourier dictionary. Consequently, the proposed method can achieve high SCE accuracy regardless of whether data are missing in the collected radar-cross-section (RCS) dataset or not. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:1408–1415, 2016

Accuracy improvement in laser stripe extraction for large-scale triangulation scanning measurement system

Large-scale triangulation scanning measurement systems are widely used to measure the three-dimensional profile of large-scale components and parts. The accuracy and speed of the laser stripe center extraction are essential for guaranteeing the accuracy and efficiency of the measuring system. However, in the process of large-scale measurement, multiple factors can cause deviation of the laser stripe center, including the spatial light intensity distribution, material reflectivity characteristics, and spatial transmission characteristics. A center extraction method is proposed for improving the accuracy of the laser stripe center extraction based on image evaluation of Gaussian fitting structural similarity and analysis of the multiple source factors. First, according to the features of the gray distribution of the laser stripe, evaluation of the Gaussian fitting structural similarity is estimated to provide a threshold value for center compensation. Then using the relationships between the gray distribution of the laser stripe and the multiple source factors, a compensation method of center extraction is presented. Finally, measurement experiments for a large-scale aviation composite component are carried out. The experimental results for this specific implementation verify the feasibility of the proposed center extraction method and the improved accuracy for large-scale triangulation scanning measurements.

A Novel Fully Polarimetric Scattering Center Extraction Method Based on Ofdm Radar Signal

Target high resolution and fully polarimetric information is of important application in such fields as remote sensing, target recognition and so on. Simultaneous polarization measurement scheme is one new method to obtain target polarization scattering matrix (PSM), in stead of traditional alternative polarization measurement scheme. Based on its basic principle, fully polarimetric OFDM waveform is designed to extract target fully polarimetric scattering centers. After establishing the received data model, the two-dimensional polarimetric Multiple Signal Classification (2D P-MUSIC) algorithm is used to extract target scattering characteristic. Simulations are performed to verify the effectiveness of this waveform and its corresponding signal processing.

Evolutionary programming CLEAN algorithm for UWB localization images of contiguous targets

In this article, a novel scattering center extraction method using genetic algorithm is proposed to deal with the ultra-wideband (UWB) localization image, which is called evolutionary programming (EP) CLEAN algorithm. Because of the UWB characters, the ideal point scattering model and EP method are used in the algorithm for optimizing the UWB localization images. After introducing the algorithm detail, the actual model is used to realize the EP CLEAN algorithm. Compared with the conventional localization imaging algorithm, this algorithm has advantages fitting the UWB characters such as accuracy, robustness, and better resolution, which are verified by the numerical simulations. Therefore the EP CLEAN algorithm could improve localization image performance to expand the UWB technique application.

Two-dimensional evolutionary programming-based CLEAN

In this work, we present a novel two-dimensional (2-D) scattering center extraction method, called 2-D evolutionary programming (EP)-based CLEAN. The 2-D EP-based CLEAN uses the ideal point scattering model and EP for optimization. Our algorithm has accuracy, robustness and better resolution than the fast Fourier transform (FFT)-based CLEAN. Further, it does not require the transformation of measured data from the polar frequency domain to the Cartesian frequency domain or to the image domain. Numerical simulations are performed to verify the characteristics of the proposed algorithm, such as resolution, robustness and accuracy.

Radar Target Recognition Using One-Dimensional Evolutionary Programming–Based Clean

In this work, we present a method for radar target recognition using an one-dimensional (1-D) evolutionary programmingbased CLEAN. This 1-D scattering center extraction method relies upon evolutionary programming and an undamped exponential model. It is accurate, robust and fast. Moreover, it is free from the resolution problems that arise in FFT-based CLEAN. Unlike with model-based techniques, the accuracy of extracted parameters is unaffected by false estimation of the number of scattering centers. Experimental results show that the 1-D evolutionary programming-based CLEAN algorithm can be successfully applied to radar target recognition with correlation-based approaches to reduce data storage, as well as with neural network-based approaches to efficiently extract feature vectors.