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High-Speed Railway Intruding Object Image Generating with Generative Adversarial Networks.

Baoqing Guo1,2, Gan Geng3,4, Liqiang Zhu3,4

  • 1School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China. bqguo@bjtu.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a new method using an improved conditional deep convolutional generative adversarial network (C-DCGAN) to generate realistic foreign object images for high-speed railway safety. The generated images enhance the training of detection systems, improving railway safety operations.

Keywords:
GANimage generatingimage translationrailway intruding object

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Railway Engineering

Background:

  • Foreign object intrusion poses a significant risk to high-speed railway safety.
  • Accurate detection of these objects is crucial for preventing accidents.
  • A scarcity of real-world intrusion samples hinders the development of effective detection methods.

Purpose of the Study:

  • To develop a novel method for generating artificial railway intruding object images.
  • To improve the quality and authenticity of synthetic training data for object detection systems.
  • To enhance the safety and reliability of high-speed railway operations through better detection capabilities.

Main Methods:

  • An improved conditional deep convolutional generative adversarial network (C-DCGAN) with a generator and multi-scale discriminators was employed.
  • A novel loss function was implemented to enhance sample quality and authenticity.
  • A scale estimation algorithm based on the gauge constant was proposed for realistic object scaling within railway scenes.

Main Results:

  • The proposed C-DCGAN model demonstrated superior performance compared to state-of-the-art methods.
  • Generated samples achieved high quality, with pixel-wise accuracy of 80.46%, mIoU of 0.65, and mAP of 0.69.
  • High diversity was observed, indicated by a Fréchet-Inception Distance (FID) score of 26.87, and real-generated pedestrian pairs reached an mIoU of 0.85.

Conclusions:

  • The developed C-DCGAN method effectively generates high-quality and diverse synthetic images of railway intruding objects.
  • The generated data significantly aids in improving the accuracy of foreign object detection systems for high-speed railways.
  • This approach offers a viable solution to the data scarcity problem, contributing to enhanced railway safety.