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A Statistical Image Feature-Based Deep Belief Network for Fire Detection
2021
Complexity
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In this paper, a statistic image feature-based deep belief network (DBN) is proposed for fire detections. ...
Firstly, for each individual image, all the statistic image features extracted from a flame and smoke image in time domain, frequency domain, and time-frequency domain are calculated to construct training ...
Deep belief network (DBN) is a kind of the generative deep learning model with powerful feature learning ability [29] . ...
doi:10.1155/2021/5554316
fatcat:vpznda7dxfdzfoyfsg4ofxexqi
Efficient Video Fire Detection Exploiting Motion-Flicker-based Dynamic Features and Deep Static Features (April 2020)
2020
IEEE Access
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Therefore, in this paper, a method that exploits both motion-flicker-based dynamic features and deep static features is proposed for video fire detection. ...
In addition, fire detection methods based on the classification of images alone using CNNs cannot account for the dynamic features of fire. ...
In recent years, a large number of neural network models have been proposed, such as convolutional neural networks (CNNs) [18] , recurrent neural networks (RNNs) [19] , and deep belief networks (DBNs ...
doi:10.1109/access.2020.2991338
fatcat:rgzizayxgrgsrku5toawrqmbcy
Measuring Invariances in Deep Networks
2009
Neural Information Processing Systems
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We find that convolutional deep belief networks learn substantially more invariant features in each layer. These results further justify the use of "deep" vs. ...
Recently, deep architectures trained in an unsupervised manner have been proposed as an automatic method for extracting useful features. ...
Andrew Saxe is supported by a Scott A. and Geraldine D. Macomber Stanford Graduate Fellowship. We would also like to thank the anonymous reviewers for their helpful comments. ...
dblp:conf/nips/GoodfellowLSLN09
fatcat:5agpsnyqonglhjpyf4gw4xvpte
Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery
2018
Sensors
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Deep learning (e.g., DCNN for Deep Convolutional Neural Network) is very effective in high-level feature learning, however, a substantial amount of training images dataset is obligatory in optimizing its ...
In this work, we proposed a new saliency detection algorithm for fast location and segmentation of core fire area in aerial images. ...
Deep belief net with a back-propagation neural network combines the advantage of unsupervised learning and supervised learning, unlike CNN, DBN cannot take a multi-dimensional image as input, images are ...
doi:10.3390/s18030712
pmid:29495504
pmcid:PMC5876738
fatcat:vbxxm7tmbbbaxlfyne7gkaql3q
A Highly Accurate Forest Fire Prediction Model Based on an Improved Dynamic Convolutional Neural Network
2022
Applied Sciences
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First, the DCNN network model was trained in combination with transfer learning, and multiple pre-trained DCNN models were used to extract features from forest fire images. ...
In this work, an improved dynamic convolutional neural network (DCNN) model to accurately identify the risk of a forest fire was established based on the traditional DCNN model. ...
Acknowledgments: We would like to thank our anonymous reviewers for their critical comments and suggestions for how to improve our manuscript. ...
doi:10.3390/app12136721
fatcat:t3lo5hh2ingflpuhr4vxcmnuny
Random Forest Feature Selection and Back Propagation Neural Network to Detect Fire Using Video
2022
Journal of Sensors
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To overcome these problems, a video fire detection hybrid method based on random forest (RF) feature selection and back propagation (BP) neural network is proposed. ...
Finally, a BP neural network model is constructed for multifeature fusion and fire recognition. ...
[6] studied the color, texture, and gray-scale statistical features of flame images and constructed a deep belief network (DBN) for fire detection. Jamali et al. ...
doi:10.1155/2022/5160050
fatcat:pjll2iqfifb35g33kvuuebntgy
Forest Fire Recognition Based on Feature Extraction from Multi-View Images
2021
Traitement du signal
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To improve the accuracy of forest fire recognition, this paper proposes a graph neural network (GNN) model based on the feature similarity of multi-view images. ...
Furthermore, a fire area feature extraction method was designed based on image segmentation, aiming to simplify the complex preprocessing of images, and effectively extract the key features from images ...
[21] applied the deep belief network (DBN) to recognize flames. Considering the volume difference between fire images and normal images, Cuomo et al. ...
doi:10.18280/ts.380324
fatcat:z4skdg2p5ncifmetxthnja446m
Diving Deep into Deep Learning:History, Evolution, Types and Applications
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Here we have made an attempt to demonstrate strong learning ability and better usage of the dataset for feature extraction by deep learning. ...
If statistics is grammar and machine learning is poetry then deep learning is the creation of Socrates. ...
ACKNOWLEDGMENT The authors would like to thank REVA University for providing the necessary facility to carry out the research work. ...
doi:10.35940/ijitee.a4865.019320
fatcat:orn2asvoxfaxvlc5iv7kec4nm4
Deep Convolutional Generative Adversarial Network and Convolutional Neural Network for Smoke Detection
2020
Complexity
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a convolutional neural network (DCG-CNN) to extract smoke features and detection. ...
Besides, we designed an improved convolutional neural network (CNN) model for extracting smoke features and smoke detection. ...
[14] proposed an image smoke detection algorithm based on a deep normalized convolutional neural network. ...
doi:10.1155/2020/6843869
fatcat:snwkxefyarfzzk5622ptiudzk4
An Efficient Smoke Detection Algorithm Based on Deep Belief Network Classifier Using Energy and Intensity Features
2020
Electronics
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In this paper, a new algorithm using the deep belief network (DBN) is designed for smoke detection. ...
inserted within a simple architecture with the deep belief network (DBN). ...
The main idea of the smoke detection method that we propose hereafter is essentially based on a deep learning technique called the deep belief network (DBN) [1, 2] . ...
doi:10.3390/electronics9091390
fatcat:3mqgp243ejgojfhrkggtvrqj4m
Flame Image Processing and Classification Using a Pre-Trained VGG16 Model in Combustion Diagnosis
2021
Sensors
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The paper presents a method combining flame image processing with a deep convolutional neural network (DCNN) that ensures high accuracy of identifying undesired combustion states. ...
It uses the empirically determined relationship between the G coefficient and the average intensity of the R image component. The pre-trained VGG16 model for classification was used. ...
For example, deep convolutional neural networks are often used for image and speech recognition and for natural language processing. Deep belief networks reduce the data dimension. ...
doi:10.3390/s21020500
pmid:33445635
fatcat:s25glaw2v5aa3czg3h47vkxcve
Editorial for Special Issue "Hyperspectral Imaging and Applications"
2019
Remote Sensing
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This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories, Data Unmixing, Spectral variability, Target Detection, Hyperspectral Image Classification ...
Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. ...
Networks Jiaojiao Li, Bobo Xi, Yunsong Li, Qian Du and Keyan Wang This paper proposes a hyperspectral classification framework based on an optimal Deep Belief Networks (DBN) and a novel texture feature ...
doi:10.3390/rs11172012
fatcat:c23u3rahgjhctowk5xwllt2qea
A Deep Neural Network for face Recognition
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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For extracting the facial features, we are using deep learning model known as Convolutional Neural Network (CNN). It is one of the best models to extract features with the highest accuracy rate . ...
In this paper to detect the suspect by extracting facial features from the captured image of the suspect from CCTV and match it with the pictures stored in the database and also to achieve an accuracy ...
A Deep Neural Network for face Recognition K. Sai krishna, G. Sreenivasa Raju, P. ...
doi:10.35940/ijitee.l1105.10812s19
fatcat:u2ufgewgxfdlhkc63kup3amxm4
Developing dually optimal LCA features in sensory and action spaces for classification
2012
2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL)
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Since, Z can be taught to represent a set of trainer specified meanings (e.g., type and location), a DN treats these meanings in a unified way for both detection and recognition for objects in dynamic ...
The Developmental Networks (DN) use Lobe Component Analysis (LCA) features developed not only from the image space X but also the action space Z. ...
ACKNOWLEDGMENT The authors would like to thank Matthew Luciw and Yuekai Wang for providing some of their programs. ...
doi:10.1109/devlrn.2012.6400885
dblp:conf/icdl-epirob/WagleW12
fatcat:4h3taeqtyvgm5j5glissewklte
GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography
2022
ISPRS International Journal of Geo-Information
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GeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for spatial analytics in Geography. ...
in a variety of image analysis and machine vision tasks. ...
Acknowledgments: The authors sincerely appreciate Yingjie Hu and Song Gao for comments on an earlier version of the manuscript.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/ijgi11070385
fatcat:yyzi46anyfcjrjuzcjfhbczo5y
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