矿井外因火灾图像凹陷度识别方法  

Recognition method of depression degree in mine external fire images

作　　者：孙继平[1] 李小伟 SUN Jiping;LI Xiaowei(School of artificial intelligence,China University of Mining and Technology-Beijing,Beijing 100083,China)

机构地区：[1]中国矿业大学(北京)人工智能学院,北京100083

出　　处：《煤炭科学技术》2025年第1期341-355,共15页Coal Science and Technology

基　　金：国家自然科学基金资助项目(52364017);国家重点研发计划资助项目(2016YFC0801800)。

摘　　要：图像监测是矿井火灾火焰主要感知方法,但受矿井光源影响。内凹度能减小摄像机的安装位置、拍摄距离和拍摄角度的影响,排除矿井光源干扰,快速识别并排除矩形光源等非弧形类光源正视图及其变形图像;但对于圆形光源和类圆形光源等弧形类干扰光源,计算量较大,识别时间较长。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。矩形度能排除矩形光源的干扰,但难以排除非矩形光源的干扰。受摄像机安装位置和拍摄角度等影响,圆形和矩形光源图像会出现变形,无法呈现理想的规则形状。因此,圆形度和矩形度算法难以排除矿井光源的干扰。揭示了火焰图像边界上存在多个凹陷区域,总凹陷区域面积较大;圆形灯、长方形灯和正方形灯等矿井实际光源图像边界上不存在凹陷区域等特点。提出矿井外因火灾图像凹陷度识别方法,计算目标图像边界总凹陷区域面积与其目标图像实际面积的比值(即图像凹陷度),根据火焰图像凹陷度数值较大,而矿井光源图像凹陷度数值较小,区分矿井光源与火焰。研究提出的凹陷度方法不受摄像机安装位置、拍摄距离和拍摄角度、矿井光源形状和大小等影响,适应性强,识别准确率高。凹陷度识别方法计算得到的矿井干扰光源图像与火焰图像凹陷度平均差值绝对值最大,波动较小,区分度最好。内凹度识别方法计算得到的矿井干扰光源图像与火焰图像内凹度平均差值绝对值较大,波动较小,区分度较好。矩形度识别方法计算得到的矿井干扰光源减去火焰图像矩形度平均差值绝对值较小,波动较大,区分度较差。圆形度识别方法计算得到的矿井干扰光源减去火焰图像圆形度平均差值绝对值最小,波动最大,区分度最差。结果表明:凹陷度识别火灾火焰图像准确率为98.2%,召回率为98.4%,准确率和召回率最好;内凹度识别�Image monitoring is the main perception method for mine fire flames,but it is affected by mine light sources.The internal con-cavity 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 compu-tational complexity is large and the recognition time is long.Circularity can eliminate interference from circular light sources,but it is dif-ficult 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,cir-cular and rectangular light source images may become distorted and unable to present ideal regular shapes.Therefore,it is difficult to elim-inate 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 re-cognition of depression degree in images of external mine fires,calculating the ratio of the total concavity area of the target image bound-ary to the actual area of the target image(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 i

关 键 词：矿井火灾 图像凹陷度 图像边界 凹陷区域 火灾监测 图像识别 

分 类 号：TD752.3[矿业工程—矿井通风与安全]