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作 者:王苏林 吕燕梅 WANG Su-lin;LV Yan-mei(Hope College,Southwest Jiaotong University,Chengdu Sichuan 610400,China)
出 处:《计算机仿真》2020年第2期154-158,共5页Computer Simulation
基 金:四川省普通高校应用型本科示范课程建设项目——《货物运输组织》(JZ516L846)。
摘 要:对超限货物运输列车侵入异物进行智能检测,能够有效降低超限超重货物运输事故的概率,对保障铁路货运安全具有重要意义。针对当前超限货物运输列车图像异物侵入检测方法受到采集频率的影响,列车高速行驶时,异物侵入检测易存在漏检的情况,提出基于背景差分和形态学腐蚀操作的超限货物运输列车异物侵入数据智能检测方法。针对超限货物运输列车运行场景抖动特点,设计抖动补偿算法,消除超限货物运输列车图像抖动的像素部分。通过背景差分算法获取超限货物运输列车图像背景,再利用背景帧与当前帧的差分获得二值图像,最后对所得二值图像进行形态学腐蚀膨胀操作和阴影消除,获取异物侵入目标区域。实验结果表明,所提方法可准确检测出超限货物运输列车侵入异物,对保证铁路运输具有重要的现实意义。The intelligent detection for intrusion of foreign bodies in transfinite freight train can effectively reduce the probability of overweight freight transport accidents.At present,the intrusion detection method is affected by the acquisition frequency,leading to high missed detection rate of foreign matter intrusion when the train is running at high speed.Therefore,an intelligent method to detect the foreign matter intrusion data of transfinite freight train based on background difference and morphological corrosion was proposed.According to the jitter characteristics of the running scene of transfinite freight train,a jitter compensation algorithm was designed to eliminate the jitter pixel of image.Moreover,the image background of transfinite freight train was obtained by the background difference algorithm.In addition,the difference between the background frame and the current frame was used to get the binary image.Finally,the morphological corrosion expansion and shadow elimination were performed on the binary image,and thus the target area of foreign matter intrusion was obtained.Simulation results prove that the proposed method can accurately detect the foreign object in transfinite freight train,which has practical significance to ensure the railway transportation.
关 键 词:超限货物运输列车 异物侵入数据 智能检测 背景差分
分 类 号:TP393.17[自动化与计算机技术—计算机应用技术]
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