Method for extracting suspected flame region based on infrared light and visible light images

Abstract

The invention discloses a method for extracting a suspected flame region based on infrared light and visible light images, and relates to the field of computer vision. In this method, brightness threshold segmentation is performed on a collected infrared image to obtain a suspected high-temperature flame region; at the same time, the infrared image and a visible light image are image-registered toobtain an affine transformation matrix, and the position of the suspected high-temperature flame region on the visible light image is obtained by combining the suspected high-temperature flame regiontherewith; and finally, a suspected flame region is obtained by flame segmentation based on the YCrCb color space, morphological processing, and connected component labeling. The method can effectively extract the more accurate suspected flame region, can guarantee the detection of the suspected flame region in the night environment at the same time, and is conducive to the all-weather flame detection.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a method for extracting suspected flame regions of infrared and visible light images. Background technique [0002] In recent years, the frequent occurrence of large-space building fires, mine fires, forest fires, and tunnel fires not only caused huge losses to human life and property, but also caused great damage to the human ecological environment. With the continuous advancement of science, people began to focus on the research of intelligent video analysis to prevent fires, so the method of flame detection and identification based on image and video analysis has been rapidly promoted. [0003] The extraction of suspected flame areas is the premise of flame recognition, and the separation of flame images from the background is the basis of fire detection, which is related to the reliability of subsequent feature extraction and the accuracy of target recognition. In the prior ar...

AI Technical Summary

Problems solved by technology

However, this method assumes that the initial background does not contain the training sequence of moving targets, which limits the conditions for using the background model; some algorithms often combine the motion detection of the flame model to eliminate interference sources, and the sensitivity is affected by the quality of the image acquisition equipment and motion detection. The advantages and disadvantages of the algorithm are limited, and the flame model of the general visible light image is only designed for the reddish and yellowish flame color, which has great limitations; moreover, the physical characteristics of the flame itself, such as heat, brightness and other information cannot be obtained Full application affects the detection efficiency to a certain extent; at the same time, the detection of suspected areas in the night environment is easily affected, and all-weather monitoring cannot be guaranteed

Images