Yanpeng Zhang, Xiaoqi Zhu, Dongya Zhu, Xia Xiao. Train Positioning Using Optical Camera Communication with BP Neural Network[J]. Chinese Journal of Lasers, 2023, 50(5): 0506003
- Chinese Journal of Lasers
- Vol. 50, Issue 5, 0506003 (2023)
Fig. 1. Detection and identification of LED-ID based on BP neural network (BPNN)
Fig. 2. Extraction process of LED-ROI. (a) Gray scale images; (b) processing of binary images; (c) closing operation; (d) obtaining LED-ROI
Fig. 3. Stripe images with different frequency features. (a) Modulated signal frequency is 1000 Hz; (b) modulated signal frequency is 1250 Hz
Fig. 4. Stripe images at different imaging distances. (a) 40 cm; (b) 50 cm; (c) 60 cm; (d) 80 cm; (e) 100 cm; (f) 130 cm
Fig. 5. Stripe images with different duty cycles. (a) Duty cycle is 80%; (b) duty cycle is 50%
Fig. 6. Process of image recognition and feature extraction of optical stripe code
Fig. 7. Principle of area feature extraction
Fig. 8. Principle of feature extraction of frequency and duty cycle for optical stripes code
Fig. 9. BP neural network (BPNN) structure of classification recognition for LED-ID
Fig. 10. Train positioning model based on optical camera communication
Fig. 11. Relationship between different coordinate systems
Fig. 12. Rotation relationship between camera coordinate system and world coordinate system
Fig. 13. Experimental platform for train positioning
Fig. 14. Two-dimensional positioning resultsat Z=0 m
Fig. 15. Two-dimensional positioning results at Z=0.2 m
Fig. 16. Two-dimensional positioning results at Z=0.4 m
Fig. 17. Two-dimensional positioning results at Z=0.6 m
Fig. 18. Comparison of mean positioning error at different vertical distances
Fig. 19. Cumulative distribution function (CDF) of positioning error
Fig. 20. Dynamic positioning trajectory
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Table 1. Experimental parameters
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