Point Cloud Data Processing Method of Cavity 3D Laser Scanner

Kai, 陈凯 Chen; Da, 张达 Zhang; Yuansheng, 张元生 Zhang

doi:10.3788/aos201333.0812003

Cited by 6 publications

(2 citation statements)

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“…According to the characteristics of 3D laser scanning data of surface cracks in mining subsidence areas, Li et al [2] applied 3D laser scanning technology to detect surface cracks in mining subsidence areas and used wavelet analysis to identify the position of surface cracks according to the characteristics of 3D laser scanning data. In order to improve the shortcomings of traditional monitoring methods for goaf, Chen et al [3] proposed a point-cloud denoising method based on a KD Tree and a registration method based on pointcloud characteristics and obtained realistic 3D morphological data of goaf through multiple scanning by a 3D laser scanner. Liu et al [4] studied a new method based on ground laser scanning and image processing technology to extract highway crack information in mining areas.…”

Section: Introductionmentioning

confidence: 99%

Tunnel Crack Detection Method and Crack Image Processing Algorithm Based on Improved Retinex and Deep Learning

Wu,

Zhang

2023

Sensors

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Tunnel cracks are the main factors that cause damage and collapse of tunnel structures. How to detect tunnel cracks efficiently and avoid safety accidents caused by tunnel cracks effectively is a research hotspot at present. In order to meet the need for efficient detection of tunnel cracks, the tunnel crack detection method based on improved Retinex and deep learning is proposed in this paper. The tunnel crack images collected by optical imaging equipment are used to improve the contrast information of tunnel crack images using the image enhancement algorithm, and this image enhancement algorithm has the function of multi-scale Retinex decomposition with improved central filtering. An improved VGG19 network model is constructed to achieve efficient segmentation of tunnel crack images through deep learning methods and then form the segmented binary image. The Zhang–Suen fast parallel-thinning method is used to obtain the skeleton map of the single-layer pixel, and the length and width information of the tunnel cracks are obtained. The feasibility and effectiveness of the proposed method are verified by experiments. Compared with other methods in the literature, the maximum deviation in the length of the tunnel crack is about 5 mm, and the maximum deviation in the width of the tunnel crack is about 0.8 mm. The experimental results show that the proposed method has a shorter detection time and higher detection accuracy. The research results of this paper can provide a strong basis for the health evaluation of tunnels.

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Section: Introductionmentioning

confidence: 99%

Tunnel Crack Detection Method and Crack Image Processing Algorithm Based on Improved Retinex and Deep Learning

Wu,

Zhang

2023

Sensors

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“…With the rapid development of optical measurement technology and three-dimensional (3-D) imaging [1][2][3], point cloud data has received substantial attention as a special information format that contains complete 3-D spatial data. The application of the 3-D image information is widespread in the fields of 3-D reconstruction for medical applications [4], 3-D object recognition, reverse engineering of mechanical components [5], virtual reality, and many others such as image processing and machine vision [6,7].…”

Section: Introductionmentioning

confidence: 99%

3-D Point Cloud Registration Algorithm Based on Greedy Projection Triangulation

2018

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To address the registration problem in current machine vision, a new three-dimensional (3-D) point cloud registration algorithm that combines fast point feature histograms (FPFH) and greedy projection triangulation is proposed. First, the feature information is comprehensively described using FPFH feature description and the local correlation of the feature information is established using greedy projection triangulation. Thereafter, the sample consensus initial alignment method is applied for initial transformation to implement initial registration. By adjusting the initial attitude between the two cloud points, the improved initial registration values can be obtained. Finally, the iterative closest point method is used to obtain a precise conversion relationship; thus, accurate registration is completed. Specific registration experiments on simple target objects and complex target objects have been performed. The registration speed increased by 1.1% and the registration accuracy increased by 27.3% to 50% in the experiment on target object. The experimental results show that the accuracy and speed of registration have been improved and the efficient registration of the target object has successfully been performed using the greedy projection triangulation, which significantly improves the efficiency of matching feature points in machine vision.

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UAV low-altitude obstacle detection based on the fusion of LiDAR and camera

Yao

Niu

et al. 2021

Auton. Intell. Syst.

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In this paper, aiming at the flying scene of the small unmanned aerial vehicle (UAV) in the low-altitude suburban environment, we choose the sensor configuration scheme of LiDAR and visible light camera, and design the static and dynamic obstacle detection algorithms based on sensor fusion. For static obstacles such as power lines and buildings in the low-altitude environment, the way that image-assisted verification of point clouds is used to fuse the contour information of the images and the depth information of the point clouds to obtain the location and size of static obstacles. For unknown dynamic obstacles such as rotary-wing UAVs, the IMM-UKF algorithm is designed to fuse the distance measurement information of point clouds and the high precision angle measurement information of image to achieve accurate estimation of the location and velocity of the dynamic obstacles. We build an experimental platform to verify the effectiveness of the obstacle detection algorithm in actual scenes and evaluate the relevant performance indexes.

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Point Cloud Data Processing Method of Cavity 3D Laser Scanner

Cited by 6 publications

References 0 publications

Tunnel Crack Detection Method and Crack Image Processing Algorithm Based on Improved Retinex and Deep Learning

Tunnel Crack Detection Method and Crack Image Processing Algorithm Based on Improved Retinex and Deep Learning

3-D Point Cloud Registration Algorithm Based on Greedy Projection Triangulation

UAV low-altitude obstacle detection based on the fusion of LiDAR and camera

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