Fire Detection Method Based On Localization Confidence And Region-Based Fully Convolutional Network

Aiming at the problem of low location accuracy and detection accuracy of fire detection, a fire detection method based on localization confidence and region-based fully convolutional network is proposed. First, expanded separable convolutions arc used to improve the receptive field, reduce the amount of model parameters, and improve the detection speed. Second, the prediction candidate frame is translated and stretched to improve the integrity of the candidate region. Then, for non-maximum suppression method, the classification confidence degree is used as a sorting standard, which leads to the error suppression problem, so as to improve the location accuracy and detection accuracy of the candidate frame. Finally, new tags arc added, they represent the weak fire with no obvious characteristics and the strong fire with obvious characteristics, respectively. The weak fire samples arc strengthened to distinguish the weak fire from the bright background, so as to reduce the sample missing rate. Experimental results show that the proposed method, based on the public fire data set of Bilkent University and the test data collected from the internet, can make the fire area detected to be more complete. The fire position is more accurate, and the fire detection rate is higher.