Structural plane recognition from three-dimensional laser scanning points using an improved region-growing algorithm based on the robust randomized Hough transform

The staggered distribution of joints and fissures in space constitutes the weak part of any rock mass.The identification of rock mass structural planes and the extraction of characteristic parameters are the basis of rock-mass integrity evaluation,which is very important for analysis of slope stability.The laser scanning technique can be used to acquire the coordinate information pertaining to each point of the structural plane,but large amount of point cloud data,uneven density distribution,and noise point interference make the identification efficiency and accuracy of different types of structural planes limited by point cloud data analysis technology.A new point cloud identification and segmentation algorithm for rock mass structural surfaces is proposed.Based on the distribution states of the original point cloud in different neighborhoods in space,the point clouds are characterized by multi-dimensional eigenvalues and calculated by the robust randomized Hough transform(RRHT).The normal vector difference and the final eigenvalue are proposed for characteristic distinction,and the identification of rock mass structural surfaces is completed through regional growth,which strengthens the difference expression of point clouds.In addition,nearest Voxel downsampling is also introduced in the RRHT calculation,which further reduces the number of sources of neighborhood noises,thereby improving the accuracy and stability of the calculation.The advantages of the method have been verified by laboratory models.The results showed that the proposed method can better achieve the segmentation and statistics of structural planes with interfaces and sharp boundaries.The method works well in the identification of joints,fissures,and other structural planes on Mangshezhai slope in the Three Gorges Reservoir area,China.It can provide a stable and effective technique for the identification and segmentation of rock mass structural planes,which is beneficial in engineering practice.

Extraction of line and rounded objects from underwater images

In the field of underwater image processing, the line and rounded objects, like mines and torpedoes, are the most common targets for rec, ognition. Before further analysis, these two image patterns need to be detected and extracted from the underwater images in real-time. Using the subpixel position, direction and curvature information of an edge provided by Zernike Orthogonal Moment （ZOM） edge detection operators, an enhanced Randomized Hough Transform （RHT） to extract straight-lines is developed. This line extraction method consists of two steps ： the rough parameters of a line are obtained robustly at first using RHT with large quantization in the Hough space and then the parameters are refined with line fitting techniques. Therefore both the robustness and high precision can be achieved simultaneously. Particularly, the problem of ellipse extraction is often computationally demanding using traditional Hough Transform, since an ellipse is characterized by five parameters. Based on the generalized K-RASAC algorithm, we develop a new ellipse extraction algorithm using the concept of quadratic curve cluster and random sampling technique. We first develop a new representation of quadratic curves, which facilitates quantization and voting for the parameter A that represents a candidate ellipse among the quadratic curves. Then, after selecting two tangent points and calculating the quadratic parameter equation, we vote for the parameter A to determine an ellipse. Thus the problem of ellipse extraction is reduced into finding the local minimum in the A accumulator array. The methods presented have been applied successfully to the extraction of lines and ellipses from synthetic and real underwater images, serving as a basic computer vision module of the underwater objects recognition system. Compared to the standard RHT line extraction method and K-RANSAC ellipse extraction method, our methods have the attractive advantages of obtaining robustness and high precision simultaneously while preserving the merits of high computation speed and small storage requirement.

Study on Detection and Recognition of Speed Signs

Speed sign detection and recognition are the important part of the driving assistant systems.Combining gradient-based random Hough transform with BP network,a method is proposed to detect and recognize speed signs.Firstly,the gradient-based random Hough transform is used to detect and locate the speed signs.Then four contour features of the 'digit' inside the speed signs are extracted and recognized using the method of BP network.The results show that this method can detect and recognize the speed signs accurately and efficiently.

Intelligent Breakage Assessment of Composite Insulators on Overhead Transmission Lines by Ellipse Detection Based on IRHT

With the development of unmanned aerial vehicle(UAV)technology,visible images are playing an important role in the maintenance of power systems.To achieve the shed breakage evaluation of composite insulators by UAV visible images,an intelligent fault assessment method is proposed.First,the composite insulators in visible light images are identified by Faster-RCNN.After image preprocessing,the image is enhanced and the noise is removed.Then,a canny operator is used to extract the edge of the sheds.An Improved Randomized Hough Transform(IRHT)is used to detect the ellipses in the edge image.The parameters of the detected ellipse,length of major axes and minor axes,center coordinates and deflection angle of major axes,are used to realize the segmentation of the composite insulator.Finally,the number of pixel points in the ellipse and the distance between the points and the ellipse boundary are used to judge whether there are breakage or cracks on the sheds.The area ratio of the breakage to the whole shed is calculated based on the number of pixel points inside the broken area.This method can be realized without a large amount of training dataset of the specific fault type and provides a technical basis for the online fault assessment of a composite insulator on overhead transmission lines.

A novel RHT-TBD approach for weak targets in HPRF radar

A novel approach, which can handle ambiguous data from weak targets, is proposed within the randomized Hough transform track-before-detect(RHT-TBD) framework. The main idea is that, without the pre-detection and ambiguity resolution step at each time step, the ambiguous measurements are mapped by the multiple hypothesis ranging(MHR) procedure. In this way, all the information, based on the relativity in time and pulse repetition frequency(PRF) domains, can be gathered among different PRFs and integrated over time via a batch procedure. The final step is to perform the RHT with all the extended measurements, and the ambiguous data is unfolded while the detection decision is confirmed at the end of the processing chain.Unlike classic methods, the new approach resolves the problem of range ambiguity and detects the true track for targets. Finally, its application is illustrated to analyze and compare the performance between the proposed approach and the existing approach. Simulation results exhibit the effectiveness of this approach.