Earticle

초록

영어

This paper proposes a real-time object detection system for railway safety using the Fast YOLO deep learning framework. Using a dataset of over 10,000 annotated images captured from onboard cameras, the system detects people, animals, and obstacles on railway tracks under various environmental conditions. Preprocessing methods including background subtraction and Gaussian modeling enhance detection robustness, achieving 15% relative improvement over baseline in low-light conditions. Experimental results demonstrate high precision (0.925 for people, 0.893 for animals, 0.878 for obstacles) with real-time processing at 38 FPS on NVIDIA GTX 1080 GPU. Our Fast YOLO implementation outperforms Faster R-CNN by 3.2- fold in speed while maintaining comparable accuracy (mAP of 0.84 vs 0.86) and surpasses SSD by 8.7% in detection accuracy. The system achieves 95.2% detection rate for stationary hazards and 91.6% for moving objects, with false positive rates below 2.3%. Field tests over 6 months demonstrated 99.7% uptime reliability and successful prevention of 12 potential incidents. The findings confirm Fast YOLO's effectiveness for automated railway safety monitoring, providing a practical solution for real-world deployment.

목차

Abstract
1. Introduction
2. Related Works
3. Methodology
3.1 Fast YOLO Architecture for Railway Safety
3.2 Object Detection Pipeline
4. System Implementation and Loss Function Design
4.1 Ethical and Privacy Consideration
5. Result
5.1 Performance Analysis
5.2 Comparative Analysis
5.3 Failure Analysis and Long-term Stability
5.4 Detection Latency Analysis
5.5 Ablation Study
5.6 Speed-Accuracy Trade-off
6. Conclusion and Future Work
References

키워드

저자

• Abdulvokhidov Botrijon Egamberdi Ugli [ Master D. student, Department of ICT Convergence Engineering, Duksung Women’s University, South Korea ]
• Suhyeon Seo [ Master D. student, Department of ICT Convergence Engineering, Duksung Women’s University, South Korea ]
• Yangkyu Lim [ Assistant Professor, Department of ICT Convergence Engineering, Duksung Women’s University, South Korea ] Corresponding Author

참고문헌

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간행물

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• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380