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A Lane Detection Method Combined Fuzzy Control with ransac algorithm 7

A Lane Detection Method Combined Fuzzy Control with RANSAC A...

7th International Conference on Power Electronics Systems and Applications - Smart Mobility, Power Transfer and Security (PESA)

作者： Xu, Y. Shan, X. Chen, B. Y. Chi, C. Lu, Z. F. Wang, Y. Q. Shenzhen Univ Coll Mechatron & Control Engn Shenzhen 518060 Peoples R China

ISBN: (纸本)9781538613863

The traditional lane detection methods based on the ransac algorithm used to cause many false detection and unable to accurately detect the lanes in complex road environment, because of the existence of interferential noise points in the set of sampling points. Aiming at these issues, this paper presents a new lane detection method combined fuzzy control with ransac algorithm. The first process of the new lane detection method is pretreatment, the purpose of which is to denoise the image preliminarily through filtering and binarization. And then it selects the region of interest (ROI) that contains lanes in the input image and extract the initial boundary candidate points of the lanes in ROI. So far, there are still a lot of irrelevant noise points in the set of lane boundary candidate points. It would analyze the relationship between the interferential noise points and the boundary points of the lane, and then remove the interferential noise points from the set of lane boundary candidate point by using the fuzzy control. After that, fit the lane model by using ransac algorithm in the set of effective lane boundary points. The experiment shows that the method proposed in this paper has high robustness and effectiveness which can accurately detect the lanes in complicated city road.

关键词： Automatic driving for intelligent vehicles Lane detection Fuzzy control ransac algorithm

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An Improved ransac algorithm Based on Similar Structure Constraints

An Improved RANSAC Algorithm Based on Similar Structure Cons...

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International Conference on Robots & Intelligent System (ICRIS)

作者： Zhu, Wenqiu Sun, Wenjing Wang, Yexiang Liu, Shaolin Xu, Keke Hunan Univ Technol Sch Comp & Commun Zhuzhou 412007 Peoples R China Key Lab Intelligent Informat Percept & Proc Techn Zhuzhou 412007 Peoples R China

ISBN: (纸本)9781509041558

An improved ransac algorithm based on structural similarity was proposed to improve the speed and accuracy of traditional ransac (Random Sample Consensus) algorithm and to reduce iterations and runtime. Firstly, BRISK (Binary Robust Invariant Scalable Keypoints) algorithm was used to extract and describe feature points. The initial match set is obtained by hamming distance feature matching. Then, mismatches are eliminated by similar structure constraints. Finally, the new match set is taken as the input of ransac to calculate the transformation matrix. The algorithm can obtain the transformation model quickly because it has purified matching points after the initial matching. Experiments show that the number of iterations and runtime of this algorithm are obviously less than the number of iterations and runtime of the traditional algorithm. So the proposed method outperforms traditional ransac (Random Sample Consensus) algorithm significantly both in iterations and speed.

关键词： ransac algorithm feature matching similar structure purified matching points

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RANdom sample consensus (ransac) algorithm for enhancing overlapped etched track counting

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IET IMAGE PROCESSING 2015年第2期9卷 97-106页

作者： Ghergherehchi, Mitra Kim, Seung Yeol Afarideh, Hossein Kim, Yoon Sang Sungkyunkwan Univ Coll Elect & Elect Engn Suwon South Korea Korea Univ Technol & Educ Sch Comp Sci & Engn Cheonan South Korea Amirkabir Univ Technol Energy Engn & Phys Dept Tehran Iran

In this study, a method to enhance the accuracy of overlapped etched track analysis is proposed. Counting tracks by eye is not an easy task and automated tracks counting systems are attractive key for this problem. This method supplements the deficiencies of the conventional track analysis method. A computer programme named KoreaTech Track Measurement System written in C++, which is the authors' previous method, has been upgraded. In the proposed track analysis method, the track images captured from solid state nuclear track detectors are geometrically analysed and the number of tracks is counted. A damaged etching track shape can be restored on the track image to improve the analysis accuracy. For track restoration, the effective points are differentiated from the damaged track image. The track image is then restored by estimating the radii (small object removal) or their axis (ellipse, circle and non-circle) using the RANdom sample consensus method. Using the restored track image, the track parameters are obtained from the ellipse and then approximated to the contour of the track image to analyse the track image. Then, the total number of tracks including the overlapped tracks is counted. To verify the proposed track analysis method, experiments using actual etching track images are conducted and the results are discussed.

关键词： random processes image enhancement image restoration solid-state nuclear track detectors nuclear engineering computing shape recognition image capture C plus plus language random sample consensus algorithm overlapped etched track counting enhancement automated track counting system KTTMS C plus captured image tracking solid state nuclear track detector SSNTD damaged etching track shape restoration track image restoration radii estimation axis estimation track analysis method ransac algorithm

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Structural Analysis and 3D Reconstruction of Underground Pipeline Systems Based on LiDAR Point Clouds

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REMOTE SENSING 2025年第2期17卷 341-341页

作者： Lai, Qiuyao Xin, Qinchuan Tian, Yuhang Chen, Xiaoyou Li, Yujie Wu, Ruohan Sun Yat Sen Univ Sch Geog & Planning Guangzhou 510275 Peoples R China

The underground pipeline is a critical component of urban water supply and drainage infrastructure. However, the absence of accurate pipe information frequently leads to construction delays and cost overruns, adversely impacting urban management and economic development. To address these challenges, the digital management of underground pipelines has become essential. Despite its importance, research on the structural analysis and reconstruction of underground pipelines remains limited, primarily due to the complexity of underground environments and the technical constraints of LiDAR technology. This study proposes a framework for reconstructing underground pipelines based on unstructured point cloud data, aiming to accurately identify and reconstruct pipe structures from complex scenes. The Random Sample Consensus (ransac) algorithm, enhanced with parameter-adaptive adjustments and subset-independent fitting strategies, is employed to fit centerline segments from the set of center points. These segments were used to reconstruct topological connections, and a Building Information Model (BIM) of the underground pipeline was generated based on the structural analysis. Experiments on actual underground scenes evaluated the method using recall rate, radius error, and deviation between point clouds and models. Results showed an 88.8% recall rate, an average relative radius error below 3%, and a deviation of 3.79 cm, demonstrating the framework's accuracy. This research provides crucial support for pipeline management and planning in smart city development.

关键词： underground pipeline 3D reconstruction point clouds building information model ransac algorithm

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Research on Concentricity Detection Method of Automobile Brake Piston Parts Based on Improved Canny algorithm

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APPLIED SCIENCES-BASEL 2025年第8期15卷 4397-4397页

作者： Li, Qinghua Zhao, Wanting Cheng, Siyuan Ji, Yi Changchun Univ Sch Mech & Vehicle Engn Changchun 130022 Peoples R China

The automotive brake piston component is an important part of the automotive brake system, and the concentricity detection of the first piston component is crucial to ensure driving safety. In this paper, an improved Canny algorithm is proposed for non-contact detection of spring concentricity of the first piston component. Firstly, the traditional Canny algorithm is improved by replacing the Gaussian filter with a bilateral filter to fully retain the edge information, and accurate edge detection results are obtained by constructing a multi-scale analysis. After obtaining the edge images, a sub-pixel edge detection method with gray moments is introduced to optimize these edges;secondly, a circle is fitted to the extracted edge points by using the ransac algorithm to determine the center position and radius of the circle;and finally, the concentricity of the first piston part is calculated based on the fitting results. The experimental results are compared with those of the CMM and the traditional Canny algorithm, and the results show that the improved Canny algorithm reduces the coaxiality error by 4% and enables effective measurement of the concentricity of the first piston assembly spring.

关键词： Canny algorithm bilateral filter multi-scale analysis ransac algorithm concentricity

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Automated Tunnel Point Cloud Segmentation and Extraction Method

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APPLIED SCIENCES-BASEL 2025年第6期15卷 2926-2926页

作者： Wang, Zhe Zhu, Zhenyi Wu, Yong Hong, Qihao Jiang, Donglai Fu, Jinbo Xu, Sifa Zhejiang Univ Technol Coll Civil Engn Hangzhou 310014 Peoples R China Zhejiang Key Lab Intelligent Control Transit Infra Hangzhou 310007 Peoples R China Zhejiang Huadong Surveying & Engn Safety Technol C Hangzhou 311500 Peoples R China

To address the issue of inaccurate tunnel segmentation caused by solely relying on point cloud coordinates, this paper proposes two algorithms, GuSAC and TMatch, along with a ring-based cross-section extraction method to achieve high-precision tunnel lining segmentation and cross-section extraction. GuSAC, based on the ransac algorithm, introduces a minimum spanning tree to reconstruct the topological structure of the tunnel design axis. By using a sliding window, it effectively distinguishes between curved and straight sections of long tunnels while removing non-tunnel structural point clouds with normal vectors, thereby enhancing the lining boundary features and significantly improving the automation level of tunnel processing. At the same time, the TMatch algorithm, which combines cluster analysis and Gaussian Mixture Models (GMMs), achieves accurate segmentation of tunnel rings and inner ring areas and further determines the tunnel cross-section position based on this segmentation result to complete the cross-section extraction. Experimental results show that the proposed method achieves a segmentation accuracy of up to 95% on a standard tunnel point cloud dataset. Compared with traditional centerline extraction methods, the proposed cross-section extraction method does not require complex parameter settings, provides more stable positioning, and demonstrates high practicality and robustness.

关键词： tunnel structure disease analysis ransac algorithm sliding window fitting clustering segmentation Gaussian mixture model scalar field

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Effective image registration model using optimized KAZE algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第11期83卷 33959-33984页

作者： Zhang, Sheng Shen, Jie Zheng, Shengnan Tang, Jingjing Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China Nanjing Inst Technol Sch Comp Engn Nanjing 211167 Peoples R China

The incompatible problem with velocity and accuracy has been restricting the application of the KAZE algorithm. In order to resolve this shortage, we propose the effective image registration model using the optimized KAZE algorithm. This effective image registration model consist of four stages. First of all, to reduce the input data of image registration, the original registration images are preprocessed by the fusion preprocessing method based on the average and the perceptual hashing algorithms. Second, to extract image features quickly, we utilize the FAST algorithm to extract image features instead of the local extremum based on the Hessian matrix and the Taylor principle. Third, in order to accelerate the velocity of image features matching, the compressed sensing principle is used to reduce the dimension of the image feature descriptors. Finally, the two-step strategy is adopted to ensure the accuracy of image registration, the step one is that the hybrid matching method based on the FLANN and the KNN algorithms is used to rough matching, and the step two is that adopt the ransac algorithm to further accurate matching. This paper utilizes two groups of the experiments to verify the effective model, the experiment results show that the effective model has velocity advantage compared with other current image registration methods, and also achieves the compatible with velocity and accuracy in the case of the highest matching score. This model provides an effective solution for the application of image registration, and also has great significance for the development of image registration.

关键词： Image registration KAZE algorithm Hashing algorithm FAST algorithm ransac algorithm

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Combining SfM and deep learning to construct 3D point cloud models of shield tunnels and Realize spatial localization of water leakages

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MEASUREMENT 2025年 250卷

作者： Qian, Jinhua Xue, Fei Wang, Tianzuo Lin, Zhongqin Cai, Mingcheng Shou, Feifeng Shaoxing Univ Coll Civil Engn Key Lab Rock Mech & Geohazards Zhejiang Prov Shaoxing 312000 Peoples R China Fuzhou Univ Coll Civil Engn Fuzhou 350108 Peoples R China Shaoxing Commun Holding Grp Co Ltd Shaoxing 312000 Peoples R China

To address the inefficiency of traditional 3D reconstruction methods for shield tunnels and their limitations in visualization and leakage localization, this study replaces the dense reconstruction process with the generation of cylindrical point clouds using ransac to extract tunnel contours from SfM-based sparse point clouds. Experiments show that the structural error of the cylindrical point cloud is only 0.47%, and modeling time is reduced by 80.6%. With its uniform and controllable point density, the cylindrical point cloud enables texture mapping through camera parameters, achieving a 190.81% improvement in texture clarity and a 49.4% reduction in overall modeling time compared to traditional methods. Deep learning is further applied for pixel-level leakage segmentation, enabling spatial annotation in the 3D model. This method provides rapid, clear 3D modeling and efficient leakage detection, aiding in spatial leakage analysis.

关键词： 3D reconstruction Shield tunnels Water leakage Leakage detection Water leakage localization ransac algorithm Deep learning

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Segmentation of Communication Cabinets Based on Point Cloud Coverage From LiDAR Point Cloud

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Wang, Jingru Wang, Cheng Xi, Xiaohuan Du, Meng Nie, Sheng Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

The settlement and tilt of a communication base station's cabinet can harm electronic components, affecting stable operation. A 3-D model measures cabinet deformation using light detection and ranging (LiDAR) data, facilitating timely maintenance. While limited studies address cabinet segmentation, existing methods, relying on line or plane detection, face challenges with interference from objects sharing features with the cabinet. This letter proposes a cabinet point cloud segmentation method that combines cluster analysis and plane point cloud coverage (PCC). The method preprocesses the point cloud, analyzes the differences in normal vector characteristics in different points neighborhoods, segments the point cloud based on the given normal vector value, performs Euclidean clustering, and extracts the planar point cloud using the ransac algorithm to judge if it meets the characteristics of the cabinet. The proposed method is validated with nine datasets, demonstrating a 98.23% average F1-score. Results confirm the algorithm's accuracy in extracting cabinet point cloud, showcasing its versatility compared to common algorithms.

关键词： Point cloud compression Vectors Laser radar Feature extraction Clustering algorithms Data mining Classification algorithms Cabinet Euclidean clustering point cloud coverage (PCC) point cloud segmentation ransac algorithm

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Evaluation of surface damage for in-service deteriorated agricultural concrete headworks using 3D point clouds by laser scanning method

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PADDY AND WATER ENVIRONMENT 2024年第2期22卷 257-269页

作者： Shibano, Kazuma Morozova, Nadezhda Ito, Yuji Shimamoto, Yuma Tachibana, Yuki Suematsu, Kakutaro Chiyoda, Atsushi Ito, Hisaya Suzuki, Tetsuya Niigata Univ Grad Sch Sci & Technol 8050 2 ChoNishi Ku Niigata 9502181 Japan Niigata Univ Fac Agr 8050 2 ChoNishi Ku Niigata 9502181 Japan Tokyo Univ Agr & Technol Inst Agr 3-5-8 Saiwai Cho Fuchu Tokyo 1340006 Japan Nihon Suiko Consultant Co Ltd 2-50 Higashi ChoOmiya Ku Saitama Saitama 3300841 Japan

In the agricultural field, concrete headworks is the most important structure for the irrigation system. In recent years, a number of agricultural concrete infrastructures aging for a long-term period have been increasing. For maintenance and management, conventional inspection methods are time-consuming and costly, such as the electromagnetic wave method and elastic wave method. The detection of surface damage is more effective, safe and reliable than before since the laser scanning method provides detailed geometric information about the structure. The fundamental studies on point cloud data have been conducted in the civil engineering fields;nevertheless, the characteristics of point cloud in agricultural infrastructures, such as dam, headworks and canal, have not been discussed. In this study, 3D point clouds are generated for a concrete irrigation structure using the laser scanning method. The characteristics of surface damage which are quantitatively evaluated using point cloud information, geometric information and intensity parameter are investigated. The types of detected damage are efflorescence and cracks. It is investigated whether point clouds generated from a single scan or multiple scans are more effective for highly accurate detection. The characteristics of surface damage are evaluated by geometric features. The distance between the fitted plane and points is calculated by ransac algorithm and roughness parameter. The amount of efflorescence is detected by the distance between the fitted plane from ransac algorithm and points. The crack is detected by the local plane fitting method. The types of damage are characterized by the intensity parameter which is related to the color, roughness and moisture of the object. The surface damage and condition are evaluated by both geometric information and intensity parameter. These results show the unique parameters of point clouds from laser scanning methods, such as geometric features and intensity parameter,

关键词： Laser scanning method Three-dimensional point clouds Concrete headworks Surface damage ransac algorithm

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