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

1,2,*, 1,2, 1,2, 1,2 and 1,2
1
School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
2
Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control Technology, Ministry of Education, Beijing Jiaotong University, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3075; https://doi.org/10.3390/s19143075
Received: 22 May 2019 / Revised: 26 June 2019 / Accepted: 7 July 2019 / Published: 11 July 2019
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
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PDF [10469 KB, uploaded 11 July 2019]
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Abstract

Foreign object intrusion is a great threat to high-speed railway safety operations. Accurate foreign object intrusion detection is particularly important. As a result of the lack of intruding foreign object samples during the operational period, artificially generated ones will greatly benefit the development of the detection methods. In this paper, we propose a novel method to generate railway intruding object images based on an improved conditional deep convolutional generative adversarial network (C-DCGAN). It consists of a generator and multi-scale discriminators. Loss function is also improved so as to generate samples with a high quality and authenticity. The generator is extracted in order to generate foreign object images from input semantic labels. We synthesize the generated objects to the railway scene. To make the generated objects more similar to real objects, on scale in different positions of a railway scene, a scale estimation algorithm based on the gauge constant is proposed. The experimental results on the railway intruding object dataset show that the proposed C-DCGAN model outperforms several state-of-the-art methods and achieves a higher quality (the pixel-wise accuracy, mean intersection-over-union (mIoU), and mean average precision (mAP) are 80.46%, 0.65, and 0.69, respectively) and diversity (the Fréchet-Inception Distance (FID) score is 26.87) of generated samples. The mIoU of the real-generated pedestrian pairs reaches 0.85, and indicates a higher scale of accuracy for the generated intruding objects in the railway scene. View Full-Text
Keywords: railway intruding object; image generating; image translation; GAN railway intruding object; image generating; image translation; GAN
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Guo, B.; Geng, G.; Zhu, L.; Shi, H.; Yu, Z. High-Speed Railway Intruding Object Image Generating with Generative Adversarial Networks. Sensors 2019, 19, 3075.

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