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 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 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.

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

scholarly journals Knowledge distillation in deep learning and its applications

2021 ◽  
Vol 7 ◽  
pp. e474
Author(s):  
Abdolmaged Alkhulaifi ◽  
Fahad Alsahli ◽  
Irfan Ahmad
Keyword(s):  
Deep Learning ◽  
Mobile Phones ◽  
Learning Models ◽  
Student Model ◽  
Embedded Devices ◽  
Research Directions ◽  
Resource Limited ◽  
Knowledge Distillation ◽  
Teacher Model

Deep learning based models are relatively large, and it is hard to deploy such models on resource-limited devices such as mobile phones and embedded devices. One possible solution is knowledge distillation whereby a smaller model (student model) is trained by utilizing the information from a larger model (teacher model). In this paper, we present an outlook of knowledge distillation techniques applied to deep learning models. To compare the performances of different techniques, we propose a new metric called distillation metric which compares different knowledge distillation solutions based on models' sizes and accuracy scores. Based on the survey, some interesting conclusions are drawn and presented in this paper including the current challenges and possible research directions.

scholarly journals On Determining Suitable Embedded Devices for Deep Learning Models

2021 ◽  
Author(s):  
Daniel Padilla ◽  
Hatem A. Rashwan ◽  
Domènec Savi Puig
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Embedded Systems ◽  
State Of The Art ◽  
Image Data ◽  
Important Change ◽  
Learning Models ◽  
Embedded Devices ◽  
And Performance ◽  
High Level

Deep learning (DL) networks have proven to be crucial in commercial solutions with computer vision challenges due to their abilities to extract high-level abstractions of the image data and their capabilities of being easily adapted to many applications. As a result, DL methodologies had become a de facto standard for computer vision problems yielding many new kinds of research, approaches and applications. Recently, the commercial sector is also driving to use of embedded systems to be able to execute DL models, which has caused an important change on the DL panorama and the embedded systems themselves. Consequently, in this paper, we attempt to study the state of the art of embedded systems, such as GPUs, FPGAs and Mobile SoCs, that are able to use DL techniques, to modernize the stakeholders with the new systems available in the market. Besides, we aim at helping them to determine which of these systems can be beneficial and suitable for their applications in terms of upgradeability, price, deployment and performance.

scholarly journals Deep Learning for Identifying Malicious Firmware

2021 ◽  
Author(s):  
David Noever ◽  
Samantha E. Miller Noever
Keyword(s):  
Deep Learning ◽  
Operating Systems ◽  
Deep Neural Network ◽  
Forensic Analysis ◽  
Embedded Devices ◽  
Learning Methods ◽  
Digit Recognition ◽  
Image Conversion ◽  
Thumbnail Images ◽  
The Internet Of Things

A malicious firmware update may prove devastating to the embedded devices both that make up the Internet of Things (IoT) and alsothat typically lack the same security verifications now applied to full operating systems. This work converts the binary headers of 40,000 firmware examples from bytes into 1024-pixel thumbnail images to train a deep neural network. The aim is to distinguish benign and malicious variants using modern deep learning methods without needing detailed functional or forensic analysis tools. One outcome of this image conversion enables contact with the vast machine learning literature already applied to handle digit recognition (MNIST). Another result indicates that greater than 90% accurate classifications prove possible using image-based convolutional neural networks (CNN) when combined with transfer learning methods. The envisioned CNN application would intercept firmware updates before their distribution to IoT networks and score their likelihood of containing malicious variants.

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