论文标题
匹配铁路轮廓的深度学习
Deep learning on rail profiles matching
论文作者
论文摘要
必须将在现场测量的轨道横截面配置文件与设计的轮廓匹配，以评估导轨的磨损，这对于轨道维护和铁路安全非常重要。到目前为止，要匹配的测得的铁路配置文件通常具有四个功能，即需要引入大量数据，各种截面形状，硬件造成的错误以及人类的经验，以在匹配过程中解决现场的复杂情况。但是，基于特征点或特征线的传统匹配方法无法再满足要求。为此，我们首先建立了由46386对专业手动匹配的数据组成的匹配数据集的导轨配置文件，然后提出了一种使用预训练的卷积神经网络（CNN）的一般高精度方法，用于匹配铁路配置文件。这种基于深度学习的新方法有望成为这个问题的主要方法。源代码在https://github.com/kunqi1994/deep-learning-on-rail-profile-matching上。
Matching the rail cross-section profiles measured on site with the designed profile is a must to evaluate the wear of the rail, which is very important for track maintenance and rail safety. So far, the measured rail profiles to be matched usually have four features, that is, large amount of data, diverse section shapes, hardware made errors, and human experience needs to be introduced to solve the complex situation on site during matching process. However, traditional matching methods based on feature points or feature lines could no longer meet the requirements. To this end, we first establish the rail profiles matching dataset composed of 46386 pairs of professional manual matched data, then propose a general high-precision method for rail profiles matching using pre-trained convolutional neural network (CNN). This new method based on deep learning is promising to be the dominant approach for this issue. Source code is at https://github.com/Kunqi1994/Deep-learning-on-rail-profile-matching.
