Fire detection and identification method based on visual attention mechanism

Abstract

The method of traditional fire detection has many deficiencies. Not only being susceptible to been interference by environmental factors, but also not to find a lot of information timely and effectively from the fire inspection information. At the same time also not to record the specific details when the fire breaks out. These make it difficult for subsequent fire cause investigation. Therefore, this paper puts forward a new method for fire detection and identification by using visual attention mechanism. That is, through simulation of the human visual system, visual attention mechanism can help us from a lot of complex images to quickly find the information “been worth noting” of the image to find critical information, and to eliminate a lot of useless information. The method can significantly speed up image processing and improve the accuracy of image recognition. Several experiments have been designed to verify the effectiveness of the method by using SVM learning and training. The experimental results denote that the novel algorithms based on visual attention mechanism and been mixed linear function nuclear and radial basis function nuclear improve the accuracy and speed of recognition, significantly reduces the false negative rate of fire recognition and improves the accuracy of fire detection.