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A Statistical Image Feature-Based Deep Belief Network for Fire Detection

Dali Sheng, Jinlian Deng, Wei Zhang, Jie Cai, Weisheng Zhao, Jiawei Xiang, Osnat Mokryn
2021 Complexity  
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)

Yakun Xie, Jun Zhu, Yungang Cao, Yunhao Zhang, Dejun Feng, Yuchun Zhang, Min Chen
2020 IEEE Access  
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

Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee, Andrew Y. Ng
2009 Neural Information Processing Systems  
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

Yi Zhao, Jiale Ma, Xiaohui Li, Jie Zhang
2018 Sensors  
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

Shaoxiong Zheng, Peng Gao, Weixing Wang, Xiangjun Zou
2022 Applied Sciences  
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

Jin-Xing Liang, Jian-Fu Zhao, Ning Sun, Bao-Jun Shi, Mu Zhou
2022 Journal of Sensors  
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

Di Wu, Chunjiong Zhang, Li Ji, Rong Ran, Huaiyu Wu, Yanmin Xu
2021 Traitement du signal  
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

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

Hang Yin, Yurong Wei, Hedan Liu, Shuangyin Liu, Chuanyun Liu, Yacui Gao, Zhile Yang
2020 Complexity  
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

Rabeb Kaabi, Moez Bouchouicha, Aymen Mouelhi, Mounir Sayadi, Eric Moreau
2020 Electronics  
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

Zbigniew Omiotek, Andrzej Kotyra
2021 Sensors  
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"

Chein-I Chang, Meiping Song, Junping Zhang, Chao-Cheng Wu
2019 Remote Sensing  
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

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

Nikita Wagle, Juyang Weng
2012 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL)  
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

Wenwen Li, Chia-Yu Hsu
2022 ISPRS International Journal of Geo-Information  
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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