scholarly journals Design and Implementation of Substation Operation Safety Monitoring and Management System Based on Three-Dimensional Reconstruction

2020 ◽  
Author(s):  
Yingyi Yang ◽  
Hao Wu ◽  
Fan Yang ◽  
Xiaoming Mai ◽  
Hui Chen
Keyword(s):  
Management System ◽  
Point Cloud ◽  
Control Level ◽  
Three Dimensional ◽  
Safety Monitoring ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Operational Risks ◽  
Dimensional Reconstruction ◽  
3D Scene

In order to reduce operational risks and to improve the risk management and control level in substation, a substation operation safety monitoring and management system (3D2S2M) has been structured based on three-dimensional (3D) laser modeling technology. In this paper, we introduce how to build such a system and to describe its implementation details. A 3D lidar scanning technology is used to perform a holographic scan of the whole internal area in a substation to obtain color point cloud data of buildings and all equipment. Then, a novel 3D visualization safety monitoring and management system, named 3D2S2M, is developed by performing a 3D reconstruction of the point cloud data. Based on the real 3D scene model of 3D2S2M, the method of 3D distance measurement is used to replace manual on-site investigation for improving operation and maintenance efficiency. In addition, a real-time high-accuracy localization method is proposed, in order to identify and analyze the positioning and the behavior of the personnel, and the movement trajectory of the equipment. By combining positioning information and the electronic fence that used in 3D2S2M, risk levels of the personnel (or equipment) are evaluated and the corresponding alarm is issued to prevent dangerous behavior, thereby the operation risk is reduced in substation.

Research on the Parameter Conversion of Mannequin Curve

2014 ◽  
Vol 543-547 ◽  
pp. 2656-2659
Author(s):  
Bo Ren ◽  
Ji Xin Yang ◽  
Peng Wan ◽  
Xue Heng Tao ◽  
Xue Jun Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Measuring System ◽  
Reconstruction Method ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Engineering Software ◽  
Parameter Conversion ◽  
Dimensional Reconstruction ◽  
Design Software

In order to realize the reverse design of human bodys curve, the curves parameter conversion and reconstruction based on non-contact measuring system are studied in the paper. Firstly, obtain the model of point cloud data by the non-contact measurement system, and then import the data into reverse the engineering software Geomagic. Second, process the point cloud data with the method of human characteristic curves and surfaces division, structure fitting surface, and get the three-dimensional reconstruction model of human bodys point cloud data. Lastly, import the model into the forward design software Solidworks with different methods and edit it. Then finish the parameter conversion from Geomagic to the forward design software. The reconstruction method has a good value in reverse design of the mold.

Three-Dimensional Reconstruction of Single Input Image Based on Point Cloud

2021 ◽  
Vol 87 (7) ◽  
pp. 479-484
Author(s):  
Yu Hou ◽  
Ruifeng Zhai ◽  
Xueyan Li ◽  
Junfeng Song ◽  
Xuehan Ma ◽  
...  
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Three Dimensional ◽  
Data Sets ◽  
Point Cloud Data ◽  
Three Dimensional Reconstruction ◽  
Data Set ◽  
Cloud Data ◽  
Dimensional Reconstruction ◽  
Institute Of Technology

Three-dimensional reconstruction from a single image has excellent future prospects. The use of neural networks for three-dimensional reconstruction has achieved remarkable results. Most of the current point-cloud-based three-dimensional reconstruction networks are trained using nonreal data sets and do not have good generalizability. Based on the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago ()data set of large-scale scenes, this article proposes a method for processing real data sets. The data set produced in this work can better train our network model and realize point cloud reconstruction based on a single picture of the real world. Finally, the constructed point cloud data correspond well to the corresponding three-dimensional shapes, and to a certain extent, the disadvantage of the uneven distribution of the point cloud data obtained by light detection and ranging scanning is overcome using the proposed method.

Improved Three-Dimensional Reconstruction Algorithm for Point Cloud Data

2020 ◽  
Vol 57 (2) ◽  
pp. 021102
Author(s):  
庞正雅 Pang Zhengya ◽  
周志峰 Zhou Zhifeng ◽  
王立端 Wang Liduan ◽  
叶珏磊 Ye Juelei
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Reconstruction Algorithm ◽  
Point Cloud Data ◽  
Three Dimensional Reconstruction ◽  
Cloud Data ◽  
Dimensional Reconstruction

Three-Dimensional Reconstruction of Leaves Based on Laser Point Cloud Data

2021 ◽  
Author(s):  
Zhonghua Su ◽  
Guiyun Zhou ◽  
Lihui Song ◽  
Xukun Lu ◽  
Rong Zhao ◽  
...  
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Three Dimensional Reconstruction ◽  
Cloud Data ◽  
Dimensional Reconstruction

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 884
Author(s):  
Chia-Ming Tsai ◽  
Yi-Horng Lai ◽  
Yung-Da Sun ◽  
Yu-Jen Chung ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Training Data ◽  
Network Architectures ◽  
Point Cloud Data ◽  
Data Types ◽  
Raw Data ◽  
Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

Research on Generation Rules of Chest Width Point Curve Based on 3D Female Mannequin

2013 ◽  
Vol 796 ◽  
pp. 513-518
Author(s):  
Rong Jin ◽  
Bing Fei Gu ◽  
Guo Lian Liu
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Upper Body ◽  
Secondary Development ◽  
Linear Regression Method ◽  
Point Cloud Data ◽  
Body Type ◽  
Cloud Data ◽  
Engineering Software ◽  
The Individual

In this paper 110 female undergraduates in Soochow University are measured by using 3D non-contact measurement system and manual measurement. 3D point cloud data of human body is taken as research objects by using anti-engineering software, and secondary development of point cloud data is done on the basis of optimizing point cloud data. In accordance with the definition of the human chest width points and other feature points, and in the operability of the three-dimensional point cloud data, the width, thickness, and length dimensions of the curve through the chest width point are measured. Classification of body type is done by choosing the ratio values as classification index which is the ratio between thickness and width of the curve. The generation rules of the chest curve are determined for each type by using linear regression method. Human arm model could be established by the computer automatically. Thereby the individual model of the female upper body mannequin modeling can be improved effectively.

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