Fire Detection and Recognition Optimization Based on Virtual Reality Video Image

Abstract

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.

Advances Toward the Next Generation Fire Detection: Deep LSTM Variational Autoencoder for Improved Sensitivity and Reliability

“…There can also be significant costs associated with false alarms if, for example, sprinklers are triggered unnecessarily and water damage to the building occurs. Advanced fire detections use statistical models and optimization methods to improve the detection accuracy and enhance the understanding of fire event development [1][2][3][4][5].…”

“…However, its drawback is that these small detected Zhaoyi Xu, Yanjie Guo, and Joseph H. Saleh: Preparation of Papers for IEEE Access signatures might be ambiguous and non-fire related, and as a result, the false alarm rate will be high. In short, a tradeoff is generally understood to mediate between these performance metrics of a fire detection system, its sensitivity on the one hand, and its false alarm rate (the complement of specificity) on the other hand 1 .…”

A Privacy-Preserving Iot-Based Fire Detector

“…The multi expert system was based on different contributors including color dispersion of region of interest, and similarity between consecutive frames and centroid motion. In [15], the authors used support vector machine, fed with a combination of three static features and four dynamic features, to recognize flame images. They evaluated different kernels that are traditionally used in support vector machines.…”

Tackling Small Data Challenges in Visual Fire Detection: A Deep Convolutional Generative Adversarial Network Approach

“…They are essential for monitoring both indoors and outdoors for fire signatures such as smoke, heat, and radiation, and to identify early signs of fires to trigger appropriate responses. Significant progress has been made with these technologies in the last decades in part due to advances in sensor design and related technology [1][2][3][4][5][6][7][8]. Nonetheless, important challenges with fire detection remain, and these can roughly be subsumed under two broad headings, insufficient sensitivity on the one hand, and elevated false alarm rates on the other hand [9].…”