中文摘要：

虽然基于机器视觉的铁路基础设施的自动化检测技术已经被广泛使用,然而护栏作为保障列车安全运行免受异物入侵的重要组成部分,针对护栏的缺失检测仍依靠传统的人工检视方法。本文基于全景拼接技术,获取了铁路沿线护栏的全景图,并通过提取护栏全景图的灰度均值和方差等统计特征构建了全景图像的二维直方图,在此基础上提出了基于灰度-方差的二维直方图的最大熵值分割方法,从而实现了栏杆位置的自动识别和缺损检测。实验结果验证了该方法的准确性和有效性,且取得了87.5%的查准率和92.1%的查全率。

英文摘要：

Although automatic detection technology based on machine vision for railway infrastructure has been widely used,fence,as an important safeguard against foreign invasion to ensure safe running of train,has not been detected automaticaly yet,but manually as in traditional inspection.Based on panoramic stitching techniques,we acquire the panorama of the fence along the railway,and then extract gray-level statistical features such as the mean and variance values to construct the two-dimensional statistical histogram of panoramic image.On the bases of these data,we propose a segment method using the maximum entropy of two-dimensional gray mean-variance histogram to achieve rapid fence defect detection from the fence panorama.Experimental results verify the validity and accuracy of the proposed approach and it has the precision ratio of 87.5% and recall ration of 92.1%.