Fire Detection and Recognition Optimization Based on Virtual Reality Video Image

Fire detection technology based on video images can avoid many flaws in conventional methods and detect fires. To achieve this, the support vector machine (SVM) method in machine learning theory has unique advantages, while rough set (RS) theory and SVM complement each other in application. Thus, a new classifier could be created by organically combining these methods to identify fires and provide fire warnings, yielding excellent noise suppression and promotion. Therefore, in this study, an RS is used as the front-end system for the SVM method, yielding improved performance than only SVM. Recognition time is reduced, and recognition efficiency is improved. Experiments show that the RS-SVM classifier model based on parameter optimization proposed in this paper mitigates deficiencies in overfitting and determining local extremum with excellent reliability and stability, and enhances the forecast accuracy of fires. The method also reduces false fire-detection alarms and uses fire feature selection in virtual reality (VR) video images and fire detection and recognition.

[1]  Pu Li,et al.  Image fire detection algorithms based on convolutional neural networks , 2020, Case Studies in Thermal Engineering.

[2]  Shabnam Sadeghi Esfahlani,et al.  Mixed reality and remote sensing application of unmanned aerial vehicle in fire and smoke detection , 2019, J. Ind. Inf. Integr..

[3]  Nanfeng Xiao,et al.  Fire detection and identification method based on visual attention mechanism , 2015 .

[4]  H. Kalke,et al.  Support vector machine learning applied to digital images of river ice conditions , 2018, Cold Regions Science and Technology.

[5]  Hakan Cevikalp,et al.  Large-scale image retrieval using transductive support vector machines , 2017, Comput. Vis. Image Underst..

[6]  Qixing Zhang,et al.  Wildland Forest Fire Smoke Detection Based on Faster R-CNN using Synthetic Smoke Images , 2018 .

[7]  MengChu Zhou,et al.  An Efficient Group Recommendation Model With Multiattention-Based Neural Networks , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[8]  P. Coppo,et al.  Simulation of fire detection by infrared imagers from geostationary satellites , 2015 .

[9]  Nanik Suciati,et al.  Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine , 2015 .

[10]  N. Pergola,et al.  RST-FIRES, an exportable algorithm for early-fire detection and monitoring: description, implementation, and field validation in the case of the MSG-SEVIRI sensor , 2016 .

[11]  Bin Ran,et al.  Understanding Individualization Driving States via Latent Dirichlet Allocation Model , 2019, IEEE Intelligent Transportation Systems Magazine.

[12]  Ying Sun,et al.  Intelligent human computer interaction based on non redundant EMG signal , 2020, Alexandria Engineering Journal.

[13]  Zhao Zhang,et al.  Robust image recognition by L1-norm twin-projection support vector machine , 2017, Neurocomputing.

[14]  M. Wooster,et al.  Approaches for synergistically exploiting VIIRS I- and M-Band data in regional active fire detection and FRP assessment: A demonstration with respect to agricultural residue burning in Eastern China , 2017 .

[15]  Bo Tao,et al.  Probability analysis for grasp planning facing the field of medical robotics , 2019, Measurement.

[16]  Hakil Kim,et al.  Fast fire flame detection in surveillance video using logistic regression and temporal smoothing , 2016 .

[17]  Humberto Bustince,et al.  Forest fire detection: A fuzzy system approach based on overlap indices , 2017, Appl. Soft Comput..

[18]  Helmut Oberpriller,et al.  Heterogeneous sensor arrays: Merging cameras and gas sensors into innovative fire detection systems , 2016 .

[19]  Sung Wook Baik,et al.  Early fire detection using convolutional neural networks during surveillance for effective disaster management , 2017, Neurocomputing.

[20]  Markus Loepfe,et al.  An image processing technique for fire detection in video images , 2006 .

[21]  Gang Chen,et al.  Deep ranking structural support vector machine for image tagging , 2018, Pattern Recognit. Lett..

[22]  Jong-Myon Kim,et al.  An effective four-stage smoke-detection algorithm using video images for early fire-alarm systems , 2011 .

[23]  Laijun Sun,et al.  Non-destructive classification of defective potatoes based on hyperspectral imaging and support vector machine , 2019, Infrared Physics & Technology.

[24]  Zhiwen Yu,et al.  A modified support vector machine and its application to image segmentation , 2011, Image Vis. Comput..

[25]  Qi Zhao,et al.  Endpoint prediction of BOF by flame spectrum and furnace mouth image based on fuzzy support vector machine , 2019 .

[26]  Yi Wang,et al.  Real-time forest smoke detection using hand-designed features and deep learning , 2019, Comput. Electron. Agric..

[27]  Qiang Zheng,et al.  Integrating support vector machine and graph cuts for medical image segmentation , 2018, J. Vis. Commun. Image Represent..

[28]  Deep Gupta,et al.  Nonsubsampled contourlet domain visible and infrared image fusion framework for fire detection using pulse coupled neural network and spatial fuzzy clustering , 2018, Fire Safety Journal.

[29]  Yang Yang,et al.  Image anomaly detection for IoT equipment based on deep learning , 2019, J. Vis. Commun. Image Represent..

[30]  T. Kanimozhi,et al.  An integrated approach to region based image retrieval using firefly algorithm and support vector machine , 2015, Neurocomputing.

[31]  Chandan Singh,et al.  Enhancing color image retrieval performance with feature fusion and non-linear support vector machine classifier , 2018 .

[32]  Frederick W. Williams,et al.  Video Image Fire Detection for Shipboard Use , 2006 .

[33]  Driss Mammass,et al.  Multimodal Images Classification using Dense SURF, Spectral Information and Support Vector Machine , 2019, Procedia Computer Science.

[34]  Z. Niu,et al.  An active fire detection algorithm based on multi-temporal FengYun-3C VIRR data , 2018, Remote Sensing of Environment.

[35]  Xin Xu,et al.  Multimodal Representation Learning for Recommendation in Internet of Things , 2019, IEEE Internet of Things Journal.

[36]  Cheng Wang,et al.  An Efficient Passenger-Hunting Recommendation Framework With Multitask Deep Learning , 2019, IEEE Internet of Things Journal.

[37]  Tuqiang Zhou,et al.  Analysis of commercial truck drivers' potentially dangerous driving behaviors based on 11-month digital tachograph data and multilevel modeling approach. , 2019, Accident; analysis and prevention.

[38]  Wei Li,et al.  The development and first validation of the GOES Early Fire Detection (GOES-EFD) algorithm , 2016 .

[39]  E. Zio,et al.  A resilience perspective on water transport systems: The case of Eastern Star , 2019, International Journal of Disaster Risk Reduction.

[40]  Xinping Yan,et al.  Use of HFACS and fault tree model for collision risk factors analysis of icebreaker assistance in ice-covered waters , 2019, Safety Science.