The extreme popularity over the years for railway transportation urges the
necessity to maintain efficient railway management systems around the globe.
Even though, at present, there exist a large collection of Computer Aided
Designed Railway Technical Maps (RTMs) but available only in the portable
document format (PDF). Using Deep Learning and Optical Character Recognition
techniques, this research work proposes a generic system to digitize the
relevant map component data from a given input image and create a formatted
text file per image. Out of YOLOv3, SSD and Faster-RCNN object detection models
used, Faster-RCNN yields the highest mean Average Precision (mAP) and the
highest F1 score values 0.68 and 0.76 respectively. Further it is proven from
the results obtained that, one can improve the results with OCR when the text
containing image is being sent through a sophisticated pre-processing pipeline
to remove distortions.

使用深度学习和光学字符识别技术，本研究提出了一种通用系统，可以从给定的输入图像中数字化相关的地图组件数据，并创建一个格式化的文本文件。使用 Faster-RCNN 目标检测模型可以获得最高的平均精度和最高的 F1 分数值，分别为 0.68 和 0.76。通过实验结果证明，在将包含文本的图像发送到复杂的预处理流水线中去除畸变时，可以通过 OCR 来改进结果。

铁路技术图 (RTM) 组件识别的迁移学习方法

Transfer Learning Approach for Railway Technical Map (RTM) Component  Identification

Perception tasks play a crucial role in the development of automated
operations and systems across multiple application fields. In the railway
transportation domain, these tasks can improve the safety, reliability, and
efficiency of various perations, including train localization, signal
recognition, and track discrimination. However, collecting considerable and
precisely labeled datasets for testing such novel algorithms poses extreme
challenges in the railway environment due to the severe restrictions in
accessing the infrastructures and the practical difficulties associated with
properly equipping trains with the required sensors, such as cameras and
LiDARs. The remarkable innovations of graphic engine tools offer new solutions
to craft realistic synthetic datasets. To illustrate the advantages of
employing graphic simulation for early-stage testing of perception tasks in the
railway domain, this paper presents a comparative analysis of the performance
of a SLAM algorithm applied both in a virtual synthetic environment and a
real-world scenario. The analysis leverages virtual railway environments
created with the latest version of Unreal Engine, facilitating data collection
and allowing the examination of challenging scenarios, including
low-visibility, dangerous operational modes, and complex environments. The
results highlight the feasibility and potentiality of graphic simulation to
advance perception tasks in the railway domain.

图形模拟在铁路领域感知任务中的应用优势进行比较分析，结果显示了图形模拟在提升铁路领域感知任务的可行性和潜力。