Correction: Hazard Detection Algorithm for Planetary Landing Using Semantic Segmentation

ASCEND 2020 ◽  
2020 ◽  
Author(s):  
Kento Tomita ◽  
Katherine Skinner ◽  
Keidai Iiyama ◽  
Bhavi Jagatia ◽  
Tatsuwaki Nakagawa ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Hazard Detection ◽  
Planetary Landing

Hazard Detection Algorithm for Planetary Landing Using Semantic Segmentation

ASCEND 2020 ◽  
2020 ◽  
Author(s):  
Kento Tomita ◽  
Katherine Skinner ◽  
Keidai Iiyama ◽  
Bhavi Jagatia ◽  
Tatsuwaki Nakagawa ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Hazard Detection ◽  
Planetary Landing

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.

Evaluation of the Simple Safe Site Selection (S4) Hazard Detection Algorithm using Helicopter Field Test Data

2017 ◽  
Author(s):  
Michael E. Luna ◽  
Eduardo Almeida ◽  
Gary Spiers ◽  
Carlos Y. Villalpando ◽  
Andrew E. Johnson ◽  
...  
Keyword(s):  
Field Test ◽  
Test Data ◽  
Site Selection ◽  
Detection Algorithm ◽  
Safe Site ◽  
Hazard Detection

scholarly journals A Remote Sensing Image Segmentation Method Based on Fusion Mechanism

2021 ◽  
Vol 2138 (1) ◽  
pp. 012016
Author(s):  
Shuangling Zhu ◽  
Guli Nazi·Aili Mujiang ◽  
Huxidan Jumahong ◽  
Pazi Laiti·Nuer Maiti
Keyword(s):  
Remote Sensing ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Detection Algorithm ◽  
Attention Mechanism ◽  
Segmentation Method ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Input Layer ◽  
Basic Network

Abstract A U-Net convolutional network structure is fully capable of completing the end-to-end training with extremely little data, and can achieve better results. When the convolutional network has a short link between a near input layer and a near output layer, it can implement training in a deeper, more accurate and effective way. This paper mainly proposes a high-resolution remote sensing image change detection algorithm based on dense convolutional channel attention mechanism. The detection algorithm uses U-Net network module as the basic network to extract features, combines Dense-Net dense module to enhance U-Net, and introduces dense convolution channel attention mechanism into the basic convolution unit to highlight important features, thus completing semantic segmentation of dense convolutional remote sensing images. Simulation results have verified the effectiveness and robustness of this study.

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