尽早发现矿井火灾并处置，可避免或减少人员伤亡和财产损失及次生事故的发生。井下没有日光、月光、星光及闪电等自然光源，影响矿井火灾图像识别的主要是矿井光源。圆形度能够排除圆形光源的干扰，但难以排除非圆形光源的干扰。矩形度能够排除矩形光源的干扰，但难以排除非矩形光源的干扰。在工程实际中，因摄像机的拍摄角度不同，矿井光源图像会出现变形，无法呈现理想的规则形状，使用圆形度和矩形度算法难以排除矿井光源的干扰。揭示了火焰图像轮廓外接图面积明显大于其图像实际面积，圆形灯、长方形灯和正方形灯等矿井实际光源图像轮廓外接图面积近似等于其光源图像实际面积等特点。提出基于图像内凹度的矿井火灾识别及抗干扰方法，计算目标图像面积与图像轮廓外接图面积的比值(即图像内凹度)，根据火焰图像内凹度数值较小，而矿井光源图像内凹度数值较大，区分火焰与矿井光源。提出的内凹度方法不受摄像机距检测目标距离和图像大小、摄像机安装位置和摄像机拍摄检测目标的角度、矿井光源形状等影响，适应性强，识别准确率高。实验表明，内凹度识别方法计算得到的矿井干扰光源减去火焰图像平均差值最大，波动最小，区分度最好，受摄像机拍摄角度及距离影响最小，抗干扰能力最强，准确率为91.6%。矩形度识别方法计算得到的矿井干扰光源减去火焰图像平均差值较大，波动较小，区分度较好，受摄像机拍摄角度及距离影响较小，抗干扰能力一般，准确率为72.5%。圆形度识别方法计算得到的矿井干扰光源减去火焰图像平均差值最小，波动最大，区分度最差，受摄像机拍摄距离影响较小，受摄像机拍摄角度影响大，抗干扰能力最差，准确率为12.0%。因此，提出的内凹度识别方法，优于矩形度和圆形度，区分度最好，受摄像机拍摄角度及距离影响最小，抗干扰能力最强。

Early detection and distinguish of mine fires can avoid or reduce casualties, property damage, and secondary accidents. There are no natural light sources such as sunlight, moonlight, starlight, and lightning underground, and the main factor affecting the recognition of mine fire images is the mine light source. Circularity can eliminate interference from circular light sources, but it is difficult to exclude interference from non-circular light sources. Rectangularity can eliminate interference from rectangular light sources, but it is difficult to exclude interference from non-rectangular light sources. In engineering practice, due to the different shooting angles of the camera, the image of the mine light source may deform and cannot present an ideal regular shape. It is difficult to eliminate the interference of the mine light source using circularity and rectangularity algorithms. It has been revealed that the area of the external connection graphic area of the flame image is significantly larger than the actual area of the image, and the external connection graphic area of the actual light source image of the mine, such as circular lamps, rectangular lamps, and square lamps, is approximately equal to the actual area of the light source image. In this study, a mine fire recognition and anti-interference method is proposed based on the internal concavity of image, the ratio of the target image area to the external connection graphic area of the image (i.e. internal concavity of image) is calculated, and the flames and mine light sources are distinguished based on the small concavity value in the flame image and the large concavity value in the mine light source image. The internal concavity method proposed in this paper is not affected by the distance from the camera to the detection target and the size of the image, the installation position and angle of the camera to capture the detection target, and the shape of the mine light source. It has strong adaptability and high recognition accuracy. The experiment shows that the internal concave recognition method calculates the maximum average difference between the mine interference light source and the flame image, with the smallest fluctuation and the best discrimination. It is also least affected by the camera’s shooting angle and distance, and has the strongest anti-interference ability, with an accuracy of 91.6%. The rectangular recognition method calculates the average difference between the mine interference light source and the flame image, with small fluctuations and good discrimination. It is less affected by the camera’s shooting angle and distance, and has average anti-interference ability, with an accuracy rate of 72.5%. The roundness recognition method calculates the minimum average difference between the mine interference light source and the flame image, with the highest fluctuation and the worst discrimination. It is less affected by the camera shooting distance and more affected by the camera shooting angle, and has the worst anti-interference ability, with an accuracy of 12.0%. Therefore, the internal concave recognition method proposed in this paper is superior to rectangular and circular degrees, with the best discrimination, minimal influence from camera shooting angle and distance, and the strongest anti-interference ability.