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Application and Evaluation of Image-based Information Acquisition in Railway Transportation

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Abstract

In railway transportation, the information is provided in literature and graphic styles. Generally, quite a lot information can not be obtained directly from the images. As a result, an artificial intelligence system, which can obtain information and perceive the environment, has to be established. In the driving equipment monitoring system, there is a lack of comprehensive analysis and utilization of the multiple monitoring data. This paper briefly introduces the research ideas and optimization directions of image-based data acquiring, such as template matching, support vector machine (SVM), and convolutional neural network (CNN) from the perspective of image detection. Then the characteristics, application scenarios, and possible future research directions of these three types of algorithms are compared and analyzed.

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Funding

This work is supported jointly by the National Natural Science Foundation of China under Grant 61925302, 61903021, and Beijing Natural Science Foundation L211021.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Beihang University, Beijing, 100191, China

    Haifeng Song

  2. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Xiying Song

  3. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China

    Hairong Dong

Authors
  1. Haifeng Song
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  2. Xiying Song
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  3. Hairong Dong
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Contributions

Haifeng Song contributes to make the algorithm for target detection and integrate the algorithm to our railway transportation. Xiying Song and Hairong Dong contributes to review the article and supervise our research.

Corresponding author

Correspondence to Hairong Dong.

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Song, H., Song, X. & Dong, H. Application and Evaluation of Image-based Information Acquisition in Railway Transportation. J Intell Robot Syst 106, 9 (2022). https://doi.org/10.1007/s10846-022-01652-x

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