scholarly journals LiDAR Point Cloud Recognition and Visualization with Deep Learning for Overhead Contact Inspection

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6387 ◽  
Author(s):  
Xiaohan Tu ◽  
Cheng Xu ◽  
Siping Liu ◽  
Shuai Lin ◽  
Lipei Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computational Complexity ◽  
Point Cloud ◽  
High Speed ◽  
Point Cloud Data ◽  
Embedded Devices ◽  
Cloud Data ◽  
Manual Inspection ◽  
Order Of Magnitude ◽  
Supply Systems

As overhead contact (OC) is an essential part of power supply systems in high-speed railways, it is necessary to regularly inspect and repair abnormal OC components. Relative to manual inspection, applying LiDAR (light detection and ranging) to OC inspection can improve efficiency, accuracy, and safety, but it faces challenges to efficiently and effectively segment LiDAR point cloud data and identify catenary components. Recent deep learning-based recognition methods are rarely employed to recognize OC components, because they have high computational complexity, while their accuracy needs to be improved. To track these problems, we first propose a lightweight model, RobotNet, with depthwise and pointwise convolutions and an attention module to recognize the point cloud. Second, we optimize RobotNet to accelerate its recognition speed on embedded devices using an existing compilation tool. Third, we design software to facilitate the visualization of point cloud data. Our software can not only display a large amount of point cloud data, but also visualize the details of OC components. Extensive experiments demonstrate that RobotNet recognizes OC components more accurately and efficiently than others. The inference speed of the optimized RobotNet increases by an order of magnitude. RobotNet has lower computational complexity than other studies. The visualization results also show that our recognition method is effective.

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.

scholarly journals An Innovative Detection Method of High-Speed Railway Track Slab Supporting Block Plane Based on Point Cloud Data from 3D Scanning Technology

2019 ◽  
Vol 9 (16) ◽  
pp. 3345 ◽  
Author(s):  
Chen ◽  
Qin ◽  
Xia ◽  
Bao ◽  
Huang ◽  
...  
Keyword(s):  
Point Cloud ◽  
High Speed ◽  
Detection Method ◽  
Railway Track ◽  
Point Cloud Data ◽  
3D Scanner ◽  
High Speed Railway ◽  
Cloud Data ◽  
Plane Point ◽  
Track Slab

The dimension detection of high-speed railway track slabs is one of the most important tasks before the track slabs delivery. Based on the characteristics of a 3D scanner which can acquire a large amount of measurement data continuously and rapidly in a short time, this paper uses the integration of 3D scanner and the intelligent robot to detect the CRTSIII (China Railway Track System) track slab supporting block plane, then the dense and accurate supporting block plane point cloud data is obtained, and the point cloud data is registered with the established model. An improved Random Sample Consensus (RANSAC) plane fitting algorithm is also proposed to extract the data of supporting block plane point cloud in this paper. The detection method is verified and the quality analysis of the detection results is assessed by a lot of real point cloud data obtained on site. The results show that the method can meet the quality control of CRTSIII finished track slab and the detection standard. Compared with the traditional detection methods, the detection method proposed in this paper can complete the detection of a track slab in 7 min, which greatly improves the detection efficiency, and has better reliability. The method has wide application prospects in the field of railway component detection.

scholarly journals A Deep Learning-Based Perception Algorithm Using 3D LiDAR for Autonomous Driving: Simultaneous Segmentation and Detection Network (SSADNet)

2020 ◽  
Vol 10 (13) ◽  
pp. 4486 ◽  
Author(s):  
Yongbeom Lee ◽  
Seongkeun Park
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Autonomous Driving ◽  
Light Detection And Ranging ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Lidar ◽  
Top View ◽  
Single Neural Network ◽  
Reflection Intensity

In this paper, we propose a deep learning-based perception method in autonomous driving systems using a Light Detection and Ranging(LiDAR) point cloud data, which is called a simultaneous segmentation and detection network (SSADNet). SSADNet can be used to recognize both drivable areas and obstacles, which is necessary for autonomous driving. Unlike the previous methods, where separate networks were needed for segmentation and detection, SSADNet can perform segmentation and detection simultaneously based on a single neural network. The proposed method uses point cloud data obtained from a 3D LiDAR for network input to generate a top view image consisting of three channels of distance, height, and reflection intensity. The structure of the proposed network includes a branch for segmentation and a branch for detection as well as a bridge connecting the two parts. The KITTI dataset, which is often used for experiments on autonomous driving, was used for training. The experimental results show that segmentation and detection can be performed simultaneously for drivable areas and vehicles at a quick inference speed, which is appropriate for autonomous driving systems.

scholarly journals TAILORED FEATURES FOR SEMANTIC SEGMENTATION WITH A DGCNN USING FREE TRAINING SAMPLES OF A COLORED AIRBORNE POINT CLOUD

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 339-346
Author(s):  
E. Widyaningrum ◽  
M. K. Fajari ◽  
R. C. Lindenbergh ◽  
M. Hahn
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Semantic Segmentation ◽  
Blue Color ◽  
Point Cloud Data ◽  
Dynamic Graph ◽  
Cloud Data ◽  
Training Samples ◽  
Map Generation ◽  
Fast Development

Abstract. Automation of 3D LiDAR point cloud processing is expected to increase the production rate of many applications including automatic map generation. Fast development on high-end hardware has boosted the expansion of deep learning research for 3D classification and segmentation. However, deep learning requires large amount of high quality training samples. The generation of training samples for accurate classification results, especially for airborne point cloud data, is still problematic. Moreover, which customized features should be used best for segmenting airborne point cloud data is still unclear. This paper proposes semi-automatic point cloud labelling and examines the potential of combining different tailor-made features for pointwise semantic segmentation of an airborne point cloud. We implement a Dynamic Graph CNN (DGCNN) approach to classify airborne point cloud data into four land cover classes: bare-land, trees, buildings and roads. The DGCNN architecture is chosen as this network relates two approaches, PointNet and graph CNNs, to exploit the geometric relationships between points. For experiments, we train an airborne point cloud and co-aligned orthophoto of the Surabaya city area of Indonesia to DGCNN using three different tailor-made feature combinations: points with RGB (Red, Green, Blue) color, points with original LiDAR features (Intensity, Return number, Number of returns) so-called IRN, and points with two spectral colors and Intensity (Red, Green, Intensity) so-called RGI. The overall accuracy of the testing area indicates that using RGB information gives the best segmentation results of 81.05% while IRN and RGI gives accuracy values of 76.13%, and 79.81%, respectively.

scholarly journals Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize

2021 ◽  
Vol 13 (21) ◽  
pp. 4445
Author(s):  
Behrokh Nazeri ◽  
Melba Crawford
Keyword(s):  
Deep Learning ◽  
Outlier Detection ◽  
Point Cloud ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Row Crops ◽  
Cloud Data ◽  
Local Point ◽  
Before And After ◽  
The Impact

High-resolution point cloud data acquired with a laser scanner from any platform contain random noise and outliers. Therefore, outlier detection in LiDAR data is often necessary prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of the statistical distribution of points, plant complexity, and local point densities, which are crop-dependent. The goals of this study were first to investigate approaches to minimize the impact of outliers on LiDAR acquired over agricultural row crops, and specifically for sorghum and maize breeding experiments, by an unmanned aerial vehicle (UAV) and a wheel-based ground platform; second, to evaluate the impact of existing outliers in the datasets on leaf area index (LAI) prediction using LiDAR data. Two methods were investigated to detect and remove the outliers from the plant datasets. The first was based on surface fitting to noisy point cloud data via normal and curvature estimation in a local neighborhood. The second utilized the PointCleanNet deep learning framework. Both methods were applied to individual plants and field-based datasets. To evaluate the method, an F-score was calculated for synthetic data in the controlled conditions, and LAI, the variable being predicted, was computed both before and after outlier removal for both scenarios. Results indicate that the deep learning method for outlier detection is more robust than the geometric approach to changes in point densities, level of noise, and shapes. The prediction of LAI was also improved for the wheel-based vehicle data based on the coefficient of determination (R2) and the root mean squared error (RMSE) of the residuals before and after the removal of outliers.

scholarly journals Software to Determine Sphere Center from Terrestrial Laser Scanner Data per ASTM Standard E3125-17

2018 ◽  
Vol 123 ◽  
Author(s):  
Prem Rachakonda ◽  
Bala Muralikrishnan ◽  
Luc Cournoyer ◽  
Daniel Sawyer
Keyword(s):  
Point Cloud ◽  
High Speed ◽  
3D Imaging ◽  
Laser Scanner ◽  
Imaging Systems ◽  
Point Cloud Data ◽  
Dimensional Metrology ◽  
Cloud Data ◽  
Point To Point ◽  
Point Distance

Terrestrial laser scanners (TLSs) are instruments that can measure 3D coordinates of objects at high speed using a laser, resulting in high density 3D point cloud data. The Dimensional Metrology Group (DMG) at NIST performed research to support the development of documentary standards within ASTM E57 committee on 3D imaging systems. This led to the publication of the ASTM E3125-2017 standard on point-to-point distance performance evaluation of 3D imaging systems such as TLSs. To ensure that the data from different TLS systems are processed identically, the ASTM E3125- 2017 mandates the use of a common algorithm to determine the center of a sphere from point cloud data. This paper describes this algorithm and software code is provided as a download.

scholarly journals Review: Deep Learning on 3D Point Clouds

2020 ◽  
Vol 12 (11) ◽  
pp. 1729 ◽  
Author(s):  
Saifullahi Aminu Bello ◽  
Shangshu Yu ◽  
Cheng Wang ◽  
Jibril Muhmmad Adam ◽  
Jonathan Li
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
General Structure ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Point Cloud Data ◽  
3D Vision ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Learning Techniques

A point cloud is a set of points defined in a 3D metric space. Point clouds have become one of the most significant data formats for 3D representation and are gaining increased popularity as a result of the increased availability of acquisition devices, as well as seeing increased application in areas such as robotics, autonomous driving, and augmented and virtual reality. Deep learning is now the most powerful tool for data processing in computer vision and is becoming the most preferred technique for tasks such as classification, segmentation, and detection. While deep learning techniques are mainly applied to data with a structured grid, the point cloud, on the other hand, is unstructured. The unstructuredness of point clouds makes the use of deep learning for its direct processing very challenging. This paper contains a review of the recent state-of-the-art deep learning techniques, mainly focusing on raw point cloud data. The initial work on deep learning directly with raw point cloud data did not model local regions; therefore, subsequent approaches model local regions through sampling and grouping. More recently, several approaches have been proposed that not only model the local regions but also explore the correlation between points in the local regions. From the survey, we conclude that approaches that model local regions and take into account the correlation between points in the local regions perform better. Contrary to existing reviews, this paper provides a general structure for learning with raw point clouds, and various methods were compared based on the general structure. This work also introduces the popular 3D point cloud benchmark datasets and discusses the application of deep learning in popular 3D vision tasks, including classification, segmentation, and detection.

scholarly journals Using Deep Learning in Semantic Classification for Point Cloud Data

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 37121-37130 ◽  
Author(s):  
Xuanxia Yao ◽  
Jia Guo ◽  
Juan Hu ◽  
Qixuan Cao
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Semantic Classification ◽  
Cloud Data
Sign in / Sign up

Export Citation Format

Share Document