Abstract
Detecting fires is of significance to guarantee the security of buildings and forests. However, it is difficult to fast and accurately detect fire stages in complex environment because of the large variations of the fire features of color, texture, and shapes for flame and smoke images. 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 and testing samples. Then, the constructed samples are fed into DBN to classify the multiple fire stages in complex environment. DBN can automatically learn fault features layer by layer using restricted Boltzmann machine (RBM). Experiments using the benchmark data of three groups of fire and fire-like images are classified by the present method, and the classification results are also compared with those commonly used support vector machine (SVM) and convolutional deep belief networks (CDBNs) to manifest the superiority of the classification accuracy.
Highlights
And timely detection of fires is important to save life, property, and economic losses
Fire detection methods have been developed to monitor forest fires, civil infrastructure, and industrial fires [1,2,3,4,5,6]. 23,535 fire incidents of buildings in 18 cities around the world in the year 2017 were reported by the International Association of Fire and Rescue Services (CTIF) [7]. erefore, accurately and timely detection of fires using sensors is of great significance to protect the social security [8]
Calculate feature descriptor from the raw images using feature descriptors, i.e., color moments in HSV space, statistical features of gray-scale image, statistical features of gray-level co-occurrence matrix, local binary pattern histogram, and wavelet transform decomposition (WTD)-based statistical features of grayscale image, which will be discussed in details late
Summary
And timely detection of fires is important to save life, property, and economic losses. Erefore, accurately and timely detection of fires using sensors is of great significance to protect the social security [8]. Fire features, such as heat, gas, flame, and smoke, are the most commonly used in fire detection techniques to monitor fire. Kaabi et al [32] developed a Gaussian mixture model (GMM) and the corresponding energy attitude of smoke region based on RGB rules to preprocess smoke or flame images for the DBN classification. A new fire detection method based on DBN is proposed to detect fires using smoke and flame images. DBN is employed to identify the fire status using smoke or flame images in the strong background noises and other disturbances. Comparing with other commonly used methods, the classification accuracy ratio of the proposed method is manifested by using the open-access experimental investigations
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
