Abstract: Image monitoring is the main perception method for mine fire flames, but it is affected by mine light sources. The internal concavity can reduce the influence of camera installation position, shooting distance, and shooting angle, eliminate interference from mine light sources, and quickly identify and eliminate the front view and distorted images of non arc-shaped light sources such as rectangular light sources; However, for arc-shaped interference light sources such as circular light sources and quasi circular light sources, the computational complexity is large and the recognition time is long. Circularity can eliminate interference from circular light sources, but it is difficult to eliminate interference from non-circular light sources. Rectangularity can eliminate interference from rectangular light sources, but it is difficult to eliminate interference from non rectangular light sources. Due to factors such as camera installation position and angle, circular and rectangular light source images may become distorted and unable to present ideal regular shapes. Therefore, it is difficult to eliminate the interference of mine light sources using circularity and rectangularity algorithms. It reveals that there are multiple depression areas on the boundary of the flame image, with a relatively large total depression area. However, there are no depression areas on the boundary of actual mine light source images such as circular lights, rectangular lights, and square lights. Propose a method based on the recognition of depression degree in images of external mine fires, calculating the ratio of the total concavity area of the target image boundary to the actual area of the target image (i.e. image depression degree). Based on the larger depression degree value in flame images and the smaller depression degree value in mine light source images, distinguish between mine light sources and flames. The depression degree method proposed in this article is not affected by the distance and image size between the camera and the detection target, the installation position and angle of the camera shooting the detection target, the shape of the mine light source, etc. It has strong adaptability and high recognition accuracy. The average difference in depression degree between the mine interference light source image and the flame image calculated by the depression degree recognition method has the largest absolute value, small fluctuations, and the best discrimination. The average difference in the internal concavity between the mine interference light source image and the flame image calculated by the internal concavity recognition method is relatively large in absolute value, with small fluctuations and good discrimination. The rectangular degree recognition method calculates that the absolute value of the average difference between the rectangular degree of the mine interference light source and the flame image is in the middle, with large fluctuations and good discrimination. The roundness recognition method calculates the minimum absolute value of the average difference between the roundness of the mine interference light source and the flame image, with the largest fluctuation and the worst discrimination. Experimental studies have shown that the accuracy of recognizing fire flame images based on depression degree is 98.2%, and the recall rate is 98.4%, the best accuracy and recall; The accuracy of identifying fire flame images based on the internal concavity is 92.8%, and the recall rate is 92.4%, the better accuracy and recall; The accuracy of rectangularity recognition is 86.5%, and the recall rate is 86.5%, the worse accuracy and recall; The accuracy of circularity recognition is only 35.9%, and the recall rate is 31.9%, the worst accuracy and recall.