An intelligent fire detection approach through cameras based on computer vision methods

Abstract Fire that is one of the most serious accidents in petroleum and chemical factories, may lead to considerable production losses, equipment damages and casualties. Traditional fire detection was done by operators through video cameras in petroleum and chemical facilities. However, it is an unrealistic job for the operator in a large chemical facility to find out the fire in time because there may be hundreds of video cameras installed and the operator may have multiple tasks during his/her shift. With the rapid development of computer vision, intelligent fire detection has received extensive attention from academia and industry. In this paper, we present a novel intelligent fire detection approach through video cameras for preventing fire hazards from going out of control in chemical factories and other high-fire-risk industries. The approach includes three steps: motion detection, fire detection and region classification. At first, moving objects are detected through cameras by a background subtraction method. Then the frame with moving objects is determined by a fire detection model which can output fire regions and their locations. Since false fire regions (some objects similar with fire) may be generated, a region classification model is used to identify whether it is a fire region or not. Once fire appears in any camera, the approach can detect it and output the coordinates of the fire region. Simultaneously, instant messages will be immediately sent to safety supervisors as a fire alarm. The approach can meet the needs of real-time fire detection on the precision and the speed. Its industrial deployment will help detect fire at the very early stage, facilitate the emergency management and therefore significantly contribute to loss prevention.

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