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Journaux
International Journal of Advanced Network, Monitoring and Controls
Édition 7 (2022): Edition 4 (January 2022)
Accès libre
Fine-grained Recognition of Ships Under Complex Sea Conditions
Jiaojiao Ma
Jiaojiao Ma
,
Jun Yu
Jun Yu
,
Haoqi Yang
Haoqi Yang
,
Hong Jiang
Hong Jiang
et
Wei Li
Wei Li
| 26 mai 2023
International Journal of Advanced Network, Monitoring and Controls
Édition 7 (2022): Edition 4 (January 2022)
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Publié en ligne:
26 mai 2023
Pages:
39 - 46
DOI:
https://doi.org/10.2478/ijanmc-2022-0035
Mots clés
Ship Recognition
,
Complex Sea Conditions
,
Multi-scale
,
Fine-grained
,
Deep Learning
© 2022 Jiaojiao Ma et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Figure 1.
Algorithm flow chart for the ship recognition.
Figure 2.
Result image after defogging.
Figure 3.
Multi-scale training sample images.
Figure 4.
Feature extraction network structure.
Figure 5.
Region proposal network structure.
Figure 6.
The architecture of proposed multi-scale Faster R-CNN for ship recognition. The simplified CNN model is surrounded by green boxes.
Figure 7.
Part of the sample images (huochuan is cargo ship, youlun is cruise ship, yuchuan is fishing ship, youting is yacht).
Figure 8.
The ROIs of some training samples.
Figure 9.
Comparison of ship recognition experiment with fog.
Figure 10.
Comparison of two algorithms in the same sea state.
Figure 11.
Recognition results under various sea states.
Comparison of recognition efficiency of the two algorithms
Detection Method
TP
FP
TN
precision/%
recall/%
Faster R-CNN
297
40
44
88.13
87.1
Our
313
18
28
94.56
91.78
Faster R-CNN Training Process
Training stage
Network
Number of iterations
1
RPN
40000
2
Fast RCNN
40000
3
RPN
80000
4
Fast RCNN
40000