Automatic laser-scanning focus detection method using printed focus pattern

1995 ◽  
Author(s):  
Kyoichi Suwa ◽  
Hiroki Tateno ◽  
Nobuyuki Irie ◽  
Shigeru Hirukawa
Keyword(s):  
Laser Scanning ◽  
Detection Method ◽  
Focus Pattern ◽  
Focus Detection

A Focus Detection Method for Ground-Based Solar Telescope

2017 ◽  
Vol 37 (7) ◽  
pp. 0711003
Author(s):  
方玉亮 Fang Yuliang ◽  
柳光乾 Liu Guangqian ◽  
金振宇 Jin Zhenyu ◽  
李鹏飞 Li Pengfei ◽  
刘 忠 Liu Zhong
Keyword(s):  
Detection Method ◽  
Solar Telescope ◽  
Focus Detection

Detection method for the depth of pavement broken block in cement concrete based on 3D laser scanning technology

2017 ◽  
Vol 46 (2) ◽  
pp. 206006
Author(s):  
曹 霆 Cao Ting ◽  
王卫星 Wang Weixing ◽  
杨 楠 Yang Nan ◽  
高 婷 Gao Ting ◽  
王峰萍 Wang Fengping
Keyword(s):  
Laser Scanning ◽  
Detection Method ◽  
3D Laser Scanning ◽  
Cement Concrete

A Non-Contact Displacement Sensor with Diffraction Grating Metrology System for Profile Measurement

2007 ◽  
Vol 364-366 ◽  
pp. 74-79
Author(s):  
Yu Rong Chen ◽  
Xu Dong Yang ◽  
Tie Bang Xie
Keyword(s):  
Diffraction Grating ◽  
Measurement Accuracy ◽  
Detection Method ◽  
Voice Coil Motor ◽  
Displacement Sensor ◽  
Profile Measurement ◽  
Focus Detection ◽  
Focusing Lens ◽  
Metrology System ◽  
Contact Displacement

Focus detection method is one of non-contact profile measurement methods. However, the measurement accuracy of current focus detection method is limited by voice coil motor adopted by it. In this paper, based on an improved Foucault focus detection method, a new non-contact displacement sensor with diffraction grating metrology system is presented. Driven by a piezoelectric actuator instead of a voice coil motor, and a diffraction grating metrology system being with it, the sensor has high measurement accuracy. During surface profile sampling, according to focusing deviation signal, the focusing lens was driven to move vertically by the piezoelectric actuator so that its focus was always located on the workpiece surface, synchronously the vertical displacement of the focusing lens was obtained by the diffraction grating metrology system as the profile height of sampling points. The displacements of all sampling points gave the whole profile of the measured surface, which can be processed by a characterization software to obtain the measurement result. The resolution of the non-contact displacement sensor was 10 nm.

scholarly journals Application of Artificial Intelligence in Human Diagnosis and Public Health Management

2021 ◽  
Author(s):  
Weiwei Wang ◽  
Xinjie Zhao ◽  
Yanshu Jia
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Detection Rate ◽  
Detection Method ◽  
Health Management ◽  
Diagnosis Rate ◽  
Missed Diagnosis ◽  
Focus Detection ◽  
Deep Learning Model ◽  
Public Health Management

Abstract To improve the diagnostic efficiency and accuracy of corona virus disease 2019 (COVID-19), and to study the application of artificial intelligence (AI) in COVID-19 diagnosis and public health management, the computer tomography (CT) image data of 200 COVID-19 patients are collected, and the image is input into the AI auxiliary diagnosis software based on the deep learning model, "uAI the COVID-19 intelligent auxiliary analysis system", for focus detection. The software automatically carries on the pneumonia focus identification and the mark in batches, and automatically calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes, and in density, the most common shadow is the ground glass opacity. The detection rate of manual detection method is 95.30%, misdiagnosis rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of AI software focus detection method based on deep learning model is 99.76%, the misdiagnosis rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, it can effectively identify COVID-19 focus and provide relevant data information of focus to provide objective data support for COVID-19 diagnosis and public health management.

scholarly journals DETECTION OF INDOOR ATTACHED EQUIPMENT FROM TLS POINT CLOUDS USING PLANAR REGION BOUNDARY

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 495-500
Author(s):  
H. Takahashi ◽  
H. Date ◽  
S. Kanai ◽  
K. Yasutake
Keyword(s):  
Laser Scanning ◽  
Detection Method ◽  
Three Dimensional ◽  
Point Clouds ◽  
Indoor Environments ◽  
Base Surface ◽  
Shape Fitting ◽  
Boundary Points ◽  
Adaptive Parameters ◽  
Three Dimensional Models

Abstract. Laser scanning technology is useful to create accurate three-dimensional models of indoor environments for applications such as maintenance, inspection, renovation, and simulations. In this paper, a detection method of indoor attached equipment such as windows, lightings, and fire alarms, from TLS point clouds, is proposed. In order to make the method robust against to the lack of points of equipment surface, a footprint of the equipment is used for detection, because the entire or a part of the footprint boundary shapes explicitly appear as the boundary of base surfaces, i.e. walls for windows, and ceilings for lightings and fire alarms. In the method, first, base surface regions are extracted from given TLS point clouds of indoor environments. Then, footprint boundary points are detected from the region boundary points. Finally, target equipment is detected by fitting or voting using given target footprint shapes. The features of our method are footprint boundary point extraction considering occlusions, shape fitting with adaptive parameters based on point intervals, and robust shape detection by voting from multiple footprint boundary candidates. The effectiveness of the proposed method is evaluated using TLS point clouds.

Non-Destructive Detection of Crack in HDPE Plate by Nanometric Change in Surface Profile

2013 ◽  
Author(s):  
Naoya Tada ◽  
Makoto Uchida ◽  
Yoshitaka Matsukawa
Keyword(s):  
Laser Scanning ◽  
Crack Detection ◽  
Detection Method ◽  
Mechanical Load ◽  
Surface Profile ◽  
Material Surface ◽  
Scanning Microscope ◽  
Height Change ◽  
Analytical Result ◽  
Non Destructive

When a mechanical load is given to a cracked material, an undulation appears in surface profile around the crack. The undulation is caused by stress-strain concentration at the crack tip and its release near the crack center. If the load is very small and within the elastic deformation range, the material recovers the original shape after unloading and no damage remains. Therefore, this surface undulation by a small mechanical load can be used for detection of crack on the material surface. In this study, non-destructive crack detection method by nanometric change in surface profile is proposed, and the experiment and related finite element analyses were carried out for notched high-density polyethylene (HDPE) plates. Non-uniform height change by a small mechanical load around the notch on the surface of HDPE plate was measured by a laser scanning microscope. The height change distribution agreed with the analytical result.

scholarly journals 3D Roughness Measurement of Failure Surface in CFA Pile Samples Using Three-Dimensional Laser Scanning

2021 ◽  
Vol 11 (6) ◽  
pp. 2713
Author(s):  
Hyungjoon Seo
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Detection Method ◽  
Three Dimensional ◽  
Point Clouds ◽  
Failure Surface ◽  
Three Dimensions ◽  
Compression Tests ◽  
Mixed Sample ◽  
Three Samples

The bearing capacity of CFA (Continuous Flight Auger) pile is not able to reach the design capacity if proper construction is not performed due to the soil collapse at the bottom of the pile. In this paper, three pile samples were prepared to simulate the bottom of the CFA pile: grouting sample; mixture of grouting and gravel; mixture of grouting and sand. The failure surfaces of each sample obtained by a uniaxial compression tests were represented as a three-dimensional point cloud by three-dimensional laser scanning. Therefore, high resolution of point clouds can be obtained to simulate the failure surfaces of three samples. The three-dimensional point cloud of each failure surface was analyzed by a plane to points histogram (P2PH) method and a roughness detection method by kernel proposed in this paper. These methods can analyze the global roughness as well as the local roughness of the three pile samples in three dimensions. The roughness features of the grouting sample, the mixed sample of grouting and sand, and the mixed sample of grouting and gravel can be distinguished by the sections where points of each sample are predominantly distributed in the histogram of the proposed method.

scholarly journals Automatic Detection Method for Large-Volume Embedded Parts Based on Laser Scanning

2020 ◽  
Vol 809 ◽  
pp. 012014
Author(s):  
Xingyi Hu ◽  
Kaixin Hu ◽  
Hongbo Fan ◽  
Lupeng Yan ◽  
Daguang Han ◽  
...  
Keyword(s):  
Large Volume ◽  
Laser Scanning ◽  
Detection Method ◽  
Automatic Detection
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