摘要
为实现矿井外因火灾的静态辨识,给出了外因火灾辨识条件下影像轮廓圆形度、矩形度计算方法和尖角特征辨别的详细实施方案。考虑到复杂的矿井摄像系统具有结构未确知、参数不完整、对成像误差存在积分效应的灰色特征,采用GM(1,1)灰色模型和新陈代谢迭代建模方法,对测量误差的演化趋势进行预测。采用火焰与摄像机的距离作为原始数据建立灰色模型,并利用最新景深数据新陈代谢进行迭代优化。结果表明,采用大基线摄像机可以降低距离测量误差,基于机器视觉和新陈代谢机制的灰色建模方法可有效提高外因火灾辨识与定位精度,在发展系数小于0.3时5步之内的预测精度在97%以上,适用于中长期预测。
In order to realize the static identification of mine external fire,the calculation methods of roundness and rectangularity of image contour were investigated and the detailed implementation scheme of sharp angle feature identification under external fire identification were given.Considering the complex mine camera system had the grey characteristics of unascertained structure,incomplete parameters and integral effect on imaging error,GM(1,1)grey model and metabolic iterative modeling method were used to predict the evolution trend of measurement error.The distance between the flame and the camera was used as the original data to build the gray model,and the latest depth-of-field data metabolism was used for iterative optimization.The results show that the large baseline camera can reduce the distance measurement error,and the grey modeling method based on machine vision and metabolic mechanism can effectively improve the accuracy of external fire identification and positioning.When the development coefficient is less than 0.3,the prediction accuracy within five steps is more than 97%,which is suitable for medium and long term prediction.
作者
李光宇
李守军
缪燕子
LI Guangyu;LI Shoujun;MIAO Yanzi(Suqian University,Suqian 223800,China;China University of Mining and Technology,Xuzhou 221008,China)
出处
《矿业安全与环保》
北大核心
2023年第2期82-87,共6页
Mining Safety & Environmental Protection
基金
国家自然科学基金项目(6197021302)
宿迁市科技计划项目(S202219,L202210)
江苏省高等学校自然科学研究项目(21KJB470030)。
