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Fine-grained Recognition of Ships Under Complex Sea Conditions

Jiaojiao Ma

Jiaojiao Ma

School of Computer Science and Engineering, Xi'an Technological UniversityXi'an, China
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Ma, Jiaojiao
, 
Jun Yu

Jun Yu

School of Computer Science and Engineering, Xi'an Technological UniversityXi'an, China
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Yu, Jun
, 
Haoqi Yang

Haoqi Yang

School of Computer Science and Engineering, Xi'an Technological UniversityXi'an, China
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Yang, Haoqi
, 
Hong Jiang

Hong Jiang

School of Computer Science and Engineering, Xi'an Technological UniversityXi'an, China
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Jiang, Hong
 and 
Wei Li

Wei Li

Technological Center, Xi'an Technological UniversityXi'an, China
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Li, Wei
  
May 26, 2023
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Published Online: May 26, 2023
Page range: 39 - 46
DOI: https://doi.org/10.2478/ijanmc-2022-0035
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© 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.
Algorithm flow chart for the ship recognition.

Figure 2.

Result image after defogging.
Result image after defogging.

Figure 3.

Multi-scale training sample images.
Multi-scale training sample images.

Figure 4.

Feature extraction network structure.
Feature extraction network structure.

Figure 5.

Region proposal network structure.
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.
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).
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.
The ROIs of some training samples.

Figure 9.

Comparison of ship recognition experiment with fog.
Comparison of ship recognition experiment with fog.

Figure 10.

Comparison of two algorithms in the same sea state.
Comparison of two algorithms in the same sea state.

Figure 11.

Recognition results under various sea states.
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
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Computer Sciences, other
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