Based on intensive study on rail surface defect, a rail surface defect inspection scheme is proposed in this paper. With high-precession structured laser sensors, the original data are collected by industrial control computer and transformed into dimensional point cloud pattern. According to analysis of the specific feature of rail surface, a registration method is proposed to re-construct the digital rail surface. To achieve a high-precision detection result, a novel curve fitting model is established and accordingly a dynamic defect detection algorithm is improved to adapt to the successive production process based on the former study. Within the detailed study process, for each single frame data, a judge method is developed for noise elimination and specific feature defect detection. Finally, by studying successive frames of data points (actually cloud points), multiple frames data are dynamically computed to judge if there is a defect area on rail surface. In this paper, to avoid the random influence of minor factors, a probability threshold is introduced to judge defect points in the algorithm, which increases the anti-interference and decreases instability in the defect detection process. To verify the effect of proposed method, an experimental bench is developed in research process. Experimental results show that the detection method has reliable stability and anti-interference possibly influenced by minor factors like oxygen layer detection and characters area detection on rail surface. Compared with traditional two dimensional defect detection methods, the algorithm proposed in this paper is less computationally intensive and more suitable for online detection on rail surface.

在对钢轨表面缺陷进行深入研究的基础上，提出了钢轨表面缺陷检测方案。使用高精密结构的激光传感器，原始数据由工业控制计算机收集，并转换为维点云模式。通过对钢轨表面特征的分析，提出一种配准方法来重构数字钢轨表面。为了获得高精度的检测结果，建立了一种新型的曲线拟合模型，并在此基础上改进了动态缺陷检测算法，以适应后续生产过程。在详细的研究过程中，针对每个单帧数据，开发了一种用于噪声消除和特定特征缺陷检测的判断方法。最后，通过研究连续的数据点帧（实际上是云点），可以动态计算多个帧数据，以判断轨道表面是否存在缺陷区域。本文为避免次要因素的随机影响，在算法中引入了概率阈值来判断缺陷点，增加了抗干扰能力，减少了缺陷检测过程中的不稳定性。为了验证所提方法的有效性，在研究过程中建立了实验台。实验结果表明，该检测方法具有稳定的稳定性和抗干扰性，可能受到诸如氧层检测和轨道表面特征区域检测等较小因素的影响。与传统的二维缺陷检测方法相比，