![find best trainslation point cloud different size find best trainslation point cloud different size](https://venturebeat.com/wp-content/uploads/2020/06/Esk_Product_Inclusion-Index.jpg)
Last but not least, the research results provide a direct and key basis for the adjustment of the scraper conveyor, which is of great significance for an intelligent coal mine working face and accurate construction of a geological information model.ĪBSTRAK Distribusi obyek bangunan merupakan aset informasi penting yang dibutuhkan untuk berbagai aplikasi perencanaan dan pembangunan. What is more, the results provide a research foundation for the application of DGCNNs in the field of energy. The results show that the DGCNN exhibits the best performance. Finally, we compare DGCNNs with PointNet and PointNet++.
![find best trainslation point cloud different size find best trainslation point cloud different size](https://venturebeat.com/wp-content/uploads/2020/07/unity-transform-2020-labeling-comlexity.jpg)
Second, on the basis of edge convolution, being the greatest innovation of DGCNNs, we analyze the influence of the number of layers, K value, and output feature dimension of edge convolution on the effect of DGCNNs segmenting the point cloud of the coal mine working face and obtaining the intersection line of the coal wall and roof. At the same time, we put forward a fast labeling method of the point cloud of the coal mine working face and an efficient training method of the depth neural network. First, in view of the characteristics of heavy dust and strong electromagnetic interference in the environment of the coal mine working face, we have installed an underground inspection robot so that we use light detection and ranging to obtain the point cloud of the coal mine working face. Therefore, we propose to use dynamic graph convolution neural networks (DGCNNs) to segment the point cloud of the coal wall and roof so as to obtain the intersection line between them. The direct method of using deep learning to segment the point cloud ignores the local feature relationship between points.
#Find best trainslation point cloud different size full
However, the indirect method of using deep learning to segment the point cloud of coal mine working face cannot make full use of the rich information provided by the point cloud data. The intersection line information of the point cloud between the coal wall and the roof can not only accurately reflect the direction information of the scraper conveyor but also provide a preliminary basis for realizing the intelligent coal mine. The proposed method can also meet the real needs of underground tunnel internal construction surveys. Compared with the other three types of classification methods in the same field, the method in this paper is more suitable for processing tunnel point cloud data and has the advantages of high classification accuracy, strong robustness, and a simple implementation process. The average accuracy of the projection plane in each experimental area is not less than 91.49%, and the average accuracy of point cloud classification is not less than 92.63%. Experiments show that this paper’s multilevel tunnel point cloud classification method can accurately extract these four types of ground objects. To verify the algorithm’s robustness, we use the proposed method to test the highway tunnel data according to the same experimental process. To verify the engineering practicability of this method, we first collected the point cloud data of a railway tunnel inside the tunnel using a rail car equipped with high-precision LiDAR and divided the data results into four sample areas for the classification test. Concurrently, the accuracy of the projection plane and the accuracy of point cloud classification are introduced to evaluate the accuracy and finally calculate the statistics of ground object information in the tunnel. This method extracts the specific ground objects, such as tracks or roads, platforms, and pipelines, on the tunnel surface and inside the tunnel step by step. Additionally, this study proposes a multilayer underground tunnel point cloud classification method that uses the hierarchical clustering structure to deal with the original tunnel point cloud.
![find best trainslation point cloud different size find best trainslation point cloud different size](https://comprarmarihuanamadrid.com/ger/wp-content/uploads/2020/09/20200904_203329-767x1024.jpg)
Thus, classifying the ground objects inside the tunnel automatically and accurately is a critical problem to be solved in a tunnel construction survey. In this study, mobile three-dimensional laser scanning technology is used to collect a tunnel’s internal point cloud data, and many unordered point cloud data are collected. When measuring a tunnel’s internal construction and performing associated data analysis, it is necessary to accurately count the size and type information of the built tunnel internal structure. A tunnel requires internal measurements after the completion of shield construction to check the real construction quality of the tunnel and provide measurement data for the next tunnel project acceptance team. Underground tunnel engineering requires complex systematic engineering.