scholarly journals A Covid-19 viral transmission prevention system for embedded devices utilising deep learning

2021 ◽  
Author(s):  
Mihai Penica ◽  
Reenu Mohandas ◽  
Mangolika Bhattacharya ◽  
Karl Vancamp ◽  
Martin Hayes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Viral Transmission ◽  
Embedded Devices ◽  
Prevention System ◽  
Transmission Prevention

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.

A Pull-Reporting Approach for Floor Opening Detection Using Deep-Learning on Embedded Devices

2021 ◽  
Author(s):  
Sharjeel Anjum ◽  
Rabia Khalid ◽  
Muhammad Khan ◽  
Numan Khan ◽  
Chansik Park
Keyword(s):  
Deep Learning ◽  
Embedded Devices

scholarly journals Research on Efficient Deep Learning Algorithm Based on ShuffleGhost in the Field of Virtual Reality

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bangtong Huang ◽  
Hongquan Zhang ◽  
Zihong Chen ◽  
Lingling Li ◽  
Lihua Shi
Keyword(s):  
Virtual Reality ◽  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Feature Maps ◽  
Embedded Devices ◽  
Feature Map ◽  
Deep Learning Algorithm ◽  
Proper Design ◽  
The Cost

Deep learning algorithms are facing the limitation in virtual reality application due to the cost of memory, computation, and real-time computation problem. Models with rigorous performance might suffer from enormous parameters and large-scale structure, and it would be hard to replant them onto embedded devices. In this paper, with the inspiration of GhostNet, we proposed an efficient structure ShuffleGhost to make use of the redundancy in feature maps to alleviate the cost of computations, as well as tackling some drawbacks of GhostNet. Since GhostNet suffers from high computation of convolution in Ghost module and shortcut, the restriction of downsampling would make it more difficult to apply Ghost module and Ghost bottleneck to other backbone. This paper proposes three new kinds of ShuffleGhost structure to tackle the drawbacks of GhostNet. The ShuffleGhost module and ShuffleGhost bottlenecks are utilized by the shuffle layer and group convolution from ShuffleNet, and they are designed to redistribute the feature maps concatenated from Ghost Feature Map and Primary Feature Map. Besides, they eliminate the gap of them and extract the features. Then, SENet layer is adopted to reduce the computation cost of group convolution, as well as evaluating the importance of the feature maps which concatenated from Ghost Feature Maps and Primary Feature Maps and giving proper weights for the feature maps. This paper conducted some experiments and proved that the ShuffleGhostV3 has smaller trainable parameters and FLOPs with the ensurance of accuracy. And with proper design, it could be more efficient in both GPU and CPU side.

Development of Disaster Prevention System Based on Deep Neural Network using Deep Learning with Dropout

2019 ◽  
Vol 91 (sp1) ◽  
pp. 186 ◽  
Author(s):  
Yeon-Joong Kim ◽  
Eisaku Yura ◽  
Tea-Woo Kim ◽  
Jong-Sung Yoon
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Disaster Prevention ◽  
Prevention System

scholarly journals An Automated Training of Deep Learning Networks by 3D Virtual Models for Object Recognition

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 496 ◽  
Author(s):  
Kamil Židek ◽  
Peter Lazorík ◽  
Ján Piteľ ◽  
Alexander Hošovský
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
3D Models ◽  
Processing Unit ◽  
Assembly Process ◽  
Learning Networks ◽  
Embedded Devices ◽  
New Approach ◽  
Virtual Models

Small series production with a high level of variability is not suitable for full automation. So, a manual assembly process must be used, which can be improved by cooperative robots and assisted by augmented reality devices. The assisted assembly process needs reliable object recognition implementation. Currently used technologies with markers do not work reliably with objects without distinctive texture, for example, screws, nuts, and washers (single colored parts). The methodology presented in the paper introduces a new approach to object detection using deep learning networks trained remotely by 3D virtual models. Remote web application generates training input datasets from virtual 3D models. This new approach was evaluated by two different neural network models (Faster RCNN Inception v2 with SSD, MobileNet V2 with SSD). The main advantage of this approach is the very fast preparation of the 2D sample training dataset from virtual 3D models. The whole process can run in Cloud. The experiments were conducted with standard parts (nuts, screws, washers) and the recognition precision achieved was comparable with training by real samples. The learned models were tested by two different embedded devices with an Android operating system: Virtual Reality (VR) glasses, Cardboard (Samsung S7), and Augmented Reality (AR) smart glasses (Epson Moverio M350). The recognition processing delays of the learned models running in embedded devices based on an ARM processor and standard x86 processing unit were also tested for performance comparison.

Deep Learning on Mobile and Embedded Devices

2020 ◽  
Vol 53 (4) ◽  
pp. 1-37 ◽  
Author(s):  
Yanjiao Chen ◽  
Baolin Zheng ◽  
Zihan Zhang ◽  
Qian Wang ◽  
Chao Shen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Embedded Devices
Sign in / Sign up

Export Citation Format

Share Document