Fire Detection Using Image Processing

Transcription

1 Fire Detection Using Image Processing Ku. R.A.Agrawal ME Student Department of CSE Babasaheb Naik college of Engineering Pusad, India Prof. S.T.Khandare Associate professor Department of CSE Babasaheb Naik college of Engineering Pusad, India Abstract Many people die and property destroy due to fire. To reduce this loss of life and property from fire, an early warning is very important. Fire increase rapidly, we want to control on that fire. Fire detection system is used to control. A fire detection system based on light detection and analysis is proposed in the paper. For detection system color models with given conditions to separate orange, yellow, and high brightness light from background and ambient light. Fire growth is analysed and calculated based on frame differences. The proposed model yield accurate detection of fire location and gives less false rate. Keywords fire detection, flame detection, fire video, color segmentation, image processing. Introduction: Fire detection causes a huge loss to human life and property, hence early detection of fire is very important. Fire detectors, smoke detectors and temperature detectors have been widely used to protect property and give warning of fires. Traditional methods like sensor based methods have many disadvantages: they have transmission delay; they are applicable mainly for indoor regions and cannot be used for outdoor regions to monitor a large area. While vision based fire detection has many advantages: a large area can be monitored, the exact location of the fire can be located and can be fabricated along with the surveillance camera. In order to facilitate earlier detection of fire, and to monitor the spread of the fire, we propose a fire detection system based on light detection. This system uses color models with given conditions to separate color. The system will trigger an audible alarm and provide visual images of the fire as a green box superimposed over the image of the fire. There is a big trend to replace conventional fire detection techniques with computer vision-based systems. This analysis is usually based on two figures of merit; shape of the region and the temporal changes of the region. The fire detection performance depends critically on the performance of the flame pixel classifier which generates seed areas on which the rest of the system operates. The flame pixel classifier is thus required to have a very high detection rate and preferably a low false alarm rate. The

2 flame pixel classification can be considered both in gray scale and color video sequences. Proposed Methods: Proposed system consists of three phase with are explain below: In the first phase, video will be collected from data source and convert video into sequences of images for further evaluation so that the process of further detection has been started. In the second phase, the image will be color segmentation. Color segmention is a technique with segment fire from its background. Fire location are detect with help of color. In the third phase, differences between two consecutive frames are calculated. This technique has been check fire growth. Frame differences is used to reduces false detection of fire and improve accuracy of over all model. 1. System Architecture: Fig.1 System Architecture The system architecture of the proposed fire detection system is shown in Fig.1. The input of the system can be real-time video from any video camera such as a web camera or it can be taken from internet. First, the expected fire and background are separated by the developed color segmentation and received fire location frames. Next, the two consecutive fire location frames are compared to check fire growth. If fire growth continues over five frames, the alarm be triggered and the computer screen will display the video with the red box superimposed the detected fire. 2. Color Image Segmentation : Image segmentation based on color model such as HSV, YCbCr, RGB, CIE L*a*b*. For Fire detection we used YCbCr and

3 HSV. The HSV color model is used to detect information related to color and brightness. The YCbCr color model is used to detect information related to brightness because it can distinguish bright images more efficiently than other color models. Fire region detection condition is as shown below where H(x, y), S(x, y), and V(x, y) are hue, saturation, and intensity value at the spatial location (x, y) respectively. Y(x, y), Cb(x, y), Cr(x, y) are luminance, red component, and blue component at the spatial location (x, y) respectively. Y_mean, Cb_mean, and Cr_mean are the mean values of luminance, blue and red components, respectively. Each frame of the video input is processed to follow the above mentioned conditions and generate output fire location images. Each pixel of the fire location images contains either 1 or 0. The pixel value 1 means the flame pixel. The pixel value 0 means the non-flame pixel. 3. Frame Differences: It is possible for fault detection if there are lighted candles, lighted matches, orange clothes, or other objects with bright orange color in the video sequences. Therefore, we want to check fire growth. Algorithm for frame differences is as shown below. Fig. 2 Frame Differences Results: Results of these system is shown in below figure

4 analysis. The obvious outcome is significant reduction in loss of life and property. In future work, objects and shapes, such as shirts, bags, or other objects with orange color, which are approaching the camera will be analysed. The change is size and intensity caused by relative proximity to the camera may cause false alarms. REFERENCES: Conclusion: Fig 3. Screen Shot Fig.4 Screen Shot We have proposed a fire-detection system. This system uses HSV and YCbCr color models with given conditions to separate orange, yellow, and high brightness from the background. Fire growth is checked based on frame differences. Accuracy from the experiments is 93.75% and accuracy of previous system is 90.73%. Our system works very well when fire occurs, by providing significantly faster detection based on light detection and [1] [Seebamrungsat, et. al.,2014] Fire Detection in the Building Using Image Processing, Third ICT International Student Project Conference [2] [M. Li, et. al., 2013], Review of fire detection technologies based on video image, JATIT, vol. 49, no. 2, pp , Mar [3] [C. Juan et. al, 2010] Multi-feature fusion based fast video flame detection, Building and Environment, vol. 45, pp , [4] [T. Celik, et. al.,2008 ] Fire detection in video sequences using a generic color model, Fire Safety Journal,2008. [5] [Y. Y. Yan, et. al, 2012] Contour extraction of flame for fire detection, Advanced Materials Research, Manufacturing Science and Technology, vol, , pp ,

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