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Fine-grained Recognition of Ships Under Complex Sea Conditions Cover
By: Jiaojiao Ma,  Jun Yu,  Haoqi Yang,  Hong Jiang and  Wei Li  
Open Access
|May 2023

Figures & Tables

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 MethodTPFPTNprecision/%recall/%
Faster R-CNN297404488.1387.1
Our313182894.5691.78

Faster R-CNN Training Process

Training stageNetworkNumber of iterations
1RPN40000
2Fast RCNN40000
3RPN80000
4Fast RCNN40000
Language: English
Page range: 39 - 46
Published on: May 26, 2023
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Jiaojiao Ma, Jun Yu, Haoqi Yang, Hong Jiang, Wei Li, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.