A human-like visual-attention-based artificial vision system for wildland firefighting assistance

Madani, Kurosh; Kachurka, Viachaslau; Sabourin, Christophe; Amarger, Valérie; Golovko, Vladimir; Rossi, Lorenza

doi:10.1007/s10489-017-1053-6

Cited by 16 publications

(9 citation statements)

References 41 publications

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“…Some of them use color rules and some use machine learning. Detailed considerations on this matter are included in [60][61][62][63][64][65]. To be able to properly select the color space and fire pixel detection rules, the use of multiple images is recommended [65].…”

Section: The Use Of An Artificial Intelligence Platform For Flame Det...mentioning

confidence: 99%

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Analysis of Flame Suppression Capabilities Using Low-Frequency Acoustic Waves and Frequency Sweeping Techniques

Wilk-Jakubowski

2021

Symmetry

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Symmetry plays a key role in the processing and analysis of not only visual but also acoustic signals in various multidisciplinary areas. New innovative and environmentally friendly methods for extinguishing flames are still being sought worldwide. One of these techniques appears to be the acoustic method. A laboratory stand was built for this purpose, which was coupled with the tested prototype of a high-power acoustic extinguisher, and then the original experiments and analyses of extinguishing effectiveness were carried out. For extinguishing, waveforms with specified parameters selected symmetrically around the frequency for which the extinguisher was designed were used. The aim of this article is to present and discuss selected measurement results concerning the possibility of flame extinguishing with the use of sinusoidal acoustic waves of low frequency (below 21 Hz), as well as with the use of frequency sweeping techniques with set parameters. Such an extinguisher can be equipped with an intelligent module so that the extinguisher may be activated automatically (without human intervention) when flames are detected. The benefits of this combination as well as the importance of image processing for flame detection are also presented in this paper. This solution, with its good fire detection and fast response, may be applicable for extinguishing firebreaks in particular.

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Section: The Use Of An Artificial Intelligence Platform For Flame Det...mentioning

confidence: 99%

“…Therefore, new algorithms for image processing, analysis, and recognition (discrimination) have been omitted from this article. Information on this topic can be found in the bibliography of the article [60][61][62][63][64][65]. A comparative summary is given in the article [66].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Flame Suppression Capabilities Using Low-Frequency Acoustic Waves and Frequency Sweeping Techniques

Wilk-Jakubowski

2021

Symmetry

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“…The robot has artificial vision through a webcam located at a height of 700 mm above the robot base. The Image Acquisition Toolbox of Simulink is used to provide the artificial vision to the robot, and students can use this powerful tool for image processing, image segmentation [36], image enhancement, visual perception [37], recognition of 3D objects [36], human-like visual-attention-based artificial vision [38], visual SLAM [39], feature extraction [40], and noise reduction, just to mention a few. Camera images are acquired using the block From Video Device (FVD) of this toolbox, which extracts their RGB values in a matrix that can be processed by the armory of matrix operators and functions of MATLAB [41].…”

Section: Artificial Visionmentioning

confidence: 99%

Recycling and Updating an Educational Robot Manipulator with Open-Hardware-Architecture

Concha

Figueroa-Rodríguez

Fuentes-Covarrubias

et al. 2020

Sensors

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This article presents a methodology to recycle and upgrade a 4-DOF educational robot manipulator with a gripper. The robot is upgraded by providing it an artificial vision that allows obtaining the position and shape of objects collected by it. A low-cost and open-source hardware solution is also proposed to achieve motion control of the robot through a decentralized control scheme. The robot joints are actuated through five direct current motors coupled to optical encoders. Each encoder signal is fed to a proportional integral derivative controller with anti-windup that employs the motor velocity provided by a state observer. The motion controller works with only two open-architecture Arduino Mega boards, which carry out data acquisition of the optical encoder signals. MATLAB-Simulink is used to implement the controller as well as a friendly graphical interface, which allows the user to interact with the manipulator. The communication between the Arduino boards and MATLAB-Simulink is performed in real-time utilizing the Arduino IO Toolbox. Through the proposed controller, the robot follows a trajectory to collect a desired object, avoiding its collision with other objects. This fact is verified through a set of experiments presented in the paper.

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“…As a result, various successful applications of deep learning have been proposed in speech recognition, computer vision, natural language processing, data visualization, etc. (Bengio et al, 2007(Bengio et al, , 2013(Bengio et al, , 2021Bengio, 2009;Larochelle et al, 2009;Erhan et al, 2010;Golovko et al, 2010;Glorot et al, 2011;Mikolov et al, 2011;Hinton et al, 2012;Madani et al, 2018;Lamb et al, 2022;Verma et al, 2022;Aguilera et al, 2023;Menezes et al, 2023;Chen et al, 2024).…”

Section: Introductionmentioning

confidence: 99%

An analytical approach for unsupervised learning rate estimation using rectified linear units

Chen,

Golovko,

Kroshchanka

et al. 2024

Front. Neurosci.

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Unsupervised learning based on restricted Boltzmann machine or autoencoders has become an important research domain in the area of neural networks. In this paper mathematical expressions to adaptive learning step calculation for RBM with ReLU transfer function are proposed. As a result, we can automatically estimate the step size that minimizes the loss function of the neural network and correspondingly update the learning step in every iteration. We give a theoretical justification for the proposed adaptive learning rate approach, which is based on the steepest descent method. The proposed technique for adaptive learning rate estimation is compared with the existing constant step and Adam methods in terms of generalization ability and loss function. We demonstrate that the proposed approach provides better performance.

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A human-like visual-attention-based artificial vision system for wildland firefighting assistance

Cited by 16 publications

References 41 publications

Analysis of Flame Suppression Capabilities Using Low-Frequency Acoustic Waves and Frequency Sweeping Techniques

Analysis of Flame Suppression Capabilities Using Low-Frequency Acoustic Waves and Frequency Sweeping Techniques

Recycling and Updating an Educational Robot Manipulator with Open-Hardware-Architecture

An analytical approach for unsupervised learning rate estimation using rectified linear units

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