The Assisted Positioning Technology for High Speed Train Based on Deep Learning
Abstract
:1. Introduction
2. Related Works
2.1. Object Detection
2.2. Sample Expansion
3. The Construction of Data Set Based on DCGAN
3.1. DCGAN Algorithm
- (a)
- The input of G is random noise z and the output of it is the generated image . The input of D is real data x and the generated image , and the output of it is and .
- (b)
- The loss function of D is calculated as follows:
- (c)
- The loss function of G is calculated as follows:
- (d)
- In the training process, G tries to generate fake data that are as similar as possible to the real data to cheat D, and D tries its best to distinguish between the generated fake data and real data. Finally, they form a game process.
- (e)
- Repeat the above steps until the network reaches Nash equilibrium [37], that is .
3.2. The Construction of Data Set
4. The Detection Model of Kilometer Post Based on an Improved SSD Algorithm
4.1. SSD Algorithm
4.2. The Detection Model of Kilometer Posts Based on an Improved SSD Algorithm
5. Analysis of the Experimental Results
5.1. The Influence of Data Set
5.2. The Influence of Model Structure and Anchor Boxes
5.3. The Analysis of Comparative Experiment and Model Stability
5.4. The Detection Result of Kilometer Posts
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Different Data Set Size | Accuracy |
---|---|
60 images | 75.65% |
500 images | 98.92% |
Different Model Structure | Accuracy | Time |
---|---|---|
ZRNet model in [31] | 97.13% | 49.69 ms |
Our model in this paper | 97.81% | 36.12 ms |
Anchor Boxes | |
---|---|
1/8 | (33.93, 50.06) (41.95, 67.44) |
1/16 | (49.94, 67.79) (53.30, 83.19) |
1/32 | (62.35, 85.61) (79.12, 113.04) |
1/64 | (209.48, 270.44) (478.66, 633.95) |
Different Anchor Boxes | Accuracy | Time |
---|---|---|
4 | 97.81% | 36.12 ms |
2 | 98.92% | 35.43 ms |
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Song, Y.; Wen, Y. The Assisted Positioning Technology for High Speed Train Based on Deep Learning. Appl. Sci. 2020, 10, 8625. https://doi.org/10.3390/app10238625
Song Y, Wen Y. The Assisted Positioning Technology for High Speed Train Based on Deep Learning. Applied Sciences. 2020; 10(23):8625. https://doi.org/10.3390/app10238625
Chicago/Turabian StyleSong, Yali, and Yinghong Wen. 2020. "The Assisted Positioning Technology for High Speed Train Based on Deep Learning" Applied Sciences 10, no. 23: 8625. https://doi.org/10.3390/app10238625
APA StyleSong, Y., & Wen, Y. (2020). The Assisted Positioning Technology for High Speed Train Based on Deep Learning. Applied Sciences, 10(23), 8625. https://doi.org/10.3390/app10238625