TU-Net and TDeepLab: Deep Learning-Based Terrain Classification Robust to Illumination Changes, Combining Visible and Thermal Imagery

2019 ◽  
Author(s):  
Yumi Iwashita ◽  
Kazuto Nakashima ◽  
Adrian Stoica ◽  
Ryo Kurazume
Keyword(s):  
Deep Learning ◽  
Terrain Classification ◽  
Thermal Imagery ◽  
Illumination Changes

scholarly journals Image editing-based data augmentation for illumination-insensitive background subtraction

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dimitrios Sakkos ◽  
Edmond S. L. Ho ◽  
Hubert P. H. Shum ◽  
Garry Elvin
Keyword(s):  
Deep Learning ◽  
Pilot Study ◽  
Background Subtraction ◽  
Data Augmentation ◽  
Point Of View ◽  
Content Type ◽  
Illumination Changes ◽  
Image Appearance ◽  
Using Data ◽  
Limited Training Samples

PurposeA core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In our pilot study published in, Sakkos:SKIMA 2019, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly but also features semantic transformations of illumination which enhance the generalisation of the model.Design/methodology/approachIn our pilot study published in SKIMA 2019, the proposed framework successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask generated randomly. In this paper, we further enhance the data augmentation framework by proposing new variations in image appearance both locally and globally.FindingsExperimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place.Originality/valueSuch data augmentation allows us to effectively train an illumination-invariant deep learning model for BGS. We further propose a post-processing method that removes noise from the output binary map of segmentation, resulting in a cleaner, more accurate segmentation map that can generalise to multiple scenes of different conditions. We show that it is possible to train deep learning models even with very limited training samples. The source code of the project is made publicly available at https://github.com/dksakkos/illumination_augmentation

scholarly journals Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2699 ◽  
Author(s):  
Redhwan Algabri ◽  
Mun-Taek Choi
Keyword(s):  
Deep Learning ◽  
Obstacle Avoidance ◽  
Learning Algorithm ◽  
Human Robot Interaction ◽  
Target Person ◽  
Single Shot ◽  
Color Feature ◽  
Mapping Algorithm ◽  
Deep Learning Algorithm ◽  
Illumination Changes

Human following is one of the fundamental functions in human–robot interaction for mobile robots. This paper shows a novel framework with state-machine control in which the robot tracks the target person in occlusion and illumination changes, as well as navigates with obstacle avoidance while following the target to the destination. People are detected and tracked using a deep learning algorithm, called Single Shot MultiBox Detector, and the target person is identified by extracting the color feature using the hue-saturation-value histogram. The robot follows the target safely to the destination using a simultaneous localization and mapping algorithm with the LIDAR sensor for obstacle avoidance. We performed intensive experiments on our human following approach in an indoor environment with multiple people and moderate illumination changes. Experimental results indicated that the robot followed the target well to the destination, showing the effectiveness and practicability of our proposed system in the given environment.

Onboard Science Autonomy for Lunar Missions: Deep-Learning Based Terrain Classification and Novelty Detection

2020 ◽  
Author(s):  
Kaizad Raimalwala ◽  
Michele Faragalli ◽  
Melissa Battler ◽  
Evan Smal ◽  
Ewan Reid ◽  
...  
Keyword(s):  
Deep Learning ◽  
Novelty Detection ◽  
Terrain Classification ◽  
Lunar Missions

SPOC: Deep Learning-based Terrain Classification for Mars Rover Missions

2016 ◽  
Author(s):  
Brandon Rothrock ◽  
Ryan Kennedy ◽  
Chris Cunningham ◽  
Jeremie Papon ◽  
Matthew Heverly ◽  
...  
Keyword(s):  
Deep Learning ◽  
Terrain Classification ◽  
Mars Rover

scholarly journals Unsupervised Man Overboard Detection Using Thermal Imagery and Spatiotemporal Autoencoders

2021 ◽  
Author(s):  
Nikolaos Bakalos ◽  
Iason Katsamenis ◽  
Eleni Eirini Karolou ◽  
Nikolaos Doulamis
Keyword(s):  
Deep Learning ◽  
Rapid Detection ◽  
Automatic Detection ◽  
Suitable Method ◽  
Learning Models ◽  
Learning Framework ◽  
Open Sea ◽  
Illumination Changes ◽  
Thermal Data ◽  
Convolutional Autoencoder

Man overboard incidents in a maritime vessel are serious accidents where the rapid detection of the even is crucial for the safe retrieval of the person. To this end, the use of deep learning models as automatic detectors of these scenarios has been tested and proven efficient, however, the use of correct capturing methods is imperative in order for the learning framework to operate well. Thermal data can be a suitable method of monitoring, as they are not affected by illumination changes and are able to operate in rough conditions, such as open sea travel. We investigate the use of a convolutional autoencoder trained over thermal data, as a mechanism for the automatic detection of man overboard scenarios. Morever, we present a dataset that was created to emulate such events and was used for training and testing the algorithm.

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