scholarly journals LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites

Data ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 40 ◽  
Author(s):  
Khashayar Asadi ◽  
Pengyu Chen ◽  
Kevin Han ◽  
Tianfu Wu ◽  
Edgar Lobaton
Keyword(s):  
Real Time ◽  
Mobile Robotics ◽  
Autonomous Robots ◽  
Semantic Segmentation ◽  
Construction Sites ◽  
Block Based ◽  
And Performance ◽  
Similar Accuracy ◽  
Residual Block ◽  
Time Segmentation

An autonomous robot that can monitor a construction site should be able to be can contextually detect its surrounding environment by recognizing objects and making decisions based on its observation. Pixel-wise semantic segmentation in real-time is vital to building an autonomous and mobile robot. However, the learning models’ size and high memory usage associated with real-time segmentation are the main challenges for mobile robotics systems that have limited computing resources. To overcome these challenges, this paper presents an efficient semantic segmentation method named LNSNet (lightweight navigable space segmentation network) that can run on embedded platforms to determine navigable space in real-time. The core of model architecture is a new block based on separable convolution which compresses the parameters of present residual block meanwhile maintaining the accuracy and performance. LNSNet is faster, has fewer parameters and less model size, while provides similar accuracy compared to existing models. A new pixel-level annotated dataset for real-time and mobile navigable space segmentation in construction environments has been constructed for the proposed method. The results demonstrate the effectiveness and efficiency that are necessary for the future development of the autonomous robotics systems.

scholarly journals A REAL-TIME SEMANTIC SEGMENTATION ALGORITHM FOR AERIAL IMAGERY

2018 ◽  
Vol 42 (1) ◽  
pp. 141-148 ◽  
Author(s):  
Y. B. Blokhinov ◽  
V. A. Gorbachev ◽  
Y. O. Rakutin ◽  
D. A. Nikitin
Keyword(s):  
Real Time ◽  
Semantic Segmentation ◽  
Time Algorithm ◽  
Original Method ◽  
Aerial Imagery ◽  
Effective Algorithm ◽  
Aerial Photos ◽  
Segmentation Methods ◽  
And Performance ◽  
Segmentation Of Images

We propose a novel effective algorithm for real-time semantic segmentation of images that has the best accuracy in its class. Based on a comparative analysis of preliminary segmentation methods, methods for calculating attributes from image segments, as well as various algorithms of machine learning, the most effective methods in terms of their accuracy and performance are identified. Based on the research results, a modular near real-time algorithm of semantic segmentation is constructed. Training and testing is performed on the ISPRS Vaihingen collection of aerial photos of the visible and IR ranges, to which a pixel map of the terrain heights is attached. An original method for obtaining a normalized nDSM for the original DSM is proposed.

scholarly journals Hardhat-Wearing Detection Based on a Lightweight Convolutional Neural Network with Multi-Scale Features and a Top-Down Module

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1868
Author(s):  
Lu Wang ◽  
Liangbin Xie ◽  
Peiyu Yang ◽  
Qingxu Deng ◽  
Shuo Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Building Materials ◽  
Extraction Process ◽  
Construction Sites ◽  
Top Down ◽  
Multi Scale ◽  
The People ◽  
Block Based

Construction sites are dangerous due to the complex interaction of workers with equipment, building materials, vehicles, etc. As a kind of protective gear, hardhats are crucial for the safety of people on construction sites. Therefore, it is necessary for administrators to identify the people that do not wear hardhats and send out alarms to them. As manual inspection is labor-intensive and expensive, it is ideal to handle this issue by a real-time automatic detector. As such, in this paper, we present an end-to-end convolutional neural network to solve the problem of detecting if workers are wearing hardhats. The proposed method focuses on localizing a person’s head and deciding whether they are wearing a hardhat. The MobileNet model is employed as the backbone network, which allows the detector to run in real time. A top-down module is leveraged to enhance the feature-extraction process. Finally, heads with and without hardhats are detected on multi-scale features using a residual-block-based prediction module. Experimental results on a dataset that we have established show that the proposed method could produce an average precision of 87.4%/89.4% at 62 frames per second for detecting people without/with a hardhat worn on the head.

Real-Time Support for Mobile Robotics

2005 ◽  
Author(s):  
Huan Li ◽  
John Sweeney ◽  
Krithi Ramamritham ◽  
Roderic Grupen ◽  
Prashant Shenoy
Keyword(s):  
Real Time ◽  
Mobile Robotics

Development of environment design support mixed reality system capable of environment estimation using deep learning

Impact ◽  
2020 ◽  
Vol 2020 (2) ◽  
pp. 9-11
Author(s):  
Tomohiro Fukuda
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Computer Games ◽  
Construction Projects ◽  
Mixed Reality ◽  
Semantic Segmentation ◽  
Environment Design ◽  
Aviation Training ◽  
Architecture And Design ◽  
World Environment

Mixed reality (MR) is rapidly becoming a vital tool, not just in gaming, but also in education, medicine, construction and environmental management. The term refers to systems in which computer-generated content is superimposed over objects in a real-world environment across one or more sensory modalities. Although most of us have heard of the use of MR in computer games, it also has applications in military and aviation training, as well as tourism, healthcare and more. In addition, it has the potential for use in architecture and design, where buildings can be superimposed in existing locations to render 3D generations of plans. However, one major challenge that remains in MR development is the issue of real-time occlusion. This refers to hiding 3D virtual objects behind real articles. Dr Tomohiro Fukuda, who is based at the Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering at Osaka University in Japan, is an expert in this field. Researchers, led by Dr Tomohiro Fukuda, are tackling the issue of occlusion in MR. They are currently developing a MR system that realises real-time occlusion by harnessing deep learning to achieve an outdoor landscape design simulation using a semantic segmentation technique. This methodology can be used to automatically estimate the visual environment prior to and after construction projects.

A lightweight network with attention decoder for real-time semantic segmentation

2021 ◽  
Author(s):  
Kang Wang ◽  
Jinfu Yang ◽  
Shuai Yuan ◽  
Mingai Li
Keyword(s):  
Real Time ◽  
Semantic Segmentation

Lighter and Faster Cross-Concatenated Multi-Scale Residual Block Based Network for Visual Saliency Prediction

2021 ◽  
Author(s):  
Sai Phani Kumar Malladi ◽  
Jayanta Mukhopadhyay ◽  
Chaker Larabi ◽  
Santanu Chaudhury
Keyword(s):  
Visual Saliency ◽  
Multi Scale ◽  
Saliency Prediction ◽  
Block Based ◽  
Residual Block

scholarly journals Real-time 2D–3D door detection and state classification on a low-power device

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

scholarly journals IEEE Access Special Section Editorial: Real-Time Machine Learning Applications in Mobile Robotics

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 89694-89698
Author(s):  
Aysegul Ucar ◽  
Jessy W. Grizzle ◽  
Maani Ghaffari ◽  
Mattias Wahde ◽  
H. Levent Akin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Mobile Robotics ◽  
Special Section ◽  
Time Machine ◽  
Machine Learning Applications
Sign in / Sign up

Export Citation Format

Share Document