Smoke detection for early forest fire in aerial photography based on GMM background and wavelet energy

Abstract

Aiming at the problem of many background interference factors in the traditional forest fire smoke monitoring video based on digital images, a detection method that can eliminate interference factors such as clouds and skylines and its specific implementation methods are proposed. The article first obtains the early forest fire smoke source detection video from close-range aerial photography of multi-rotor aircraft, and then uses the principle of multi-modal mixture Gaussian background model to establish a background model to extract the background of the early forest fire smoke video. The features of the image are high-frequency fusion, the fusion image is compared with the high-frequency energy value of the corresponding background image, if the high-frequency energy of the local area is reduced, it is determined as a suspected smoke area, and the largest connected area of suspected smoke is marked. Through the simulation experiment of the above method, the experimental results show that the method can accurately detect the smoke in the video frame image, the false detection rate is low, and the influence of related interference factors on the detection accuracy of the early forest fire smoke video image can be eliminated.