<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>For the traditional deep learning cannot solve the fog, coastal background interference, and the difficulty of small ships recognition, a multi-scale deep learning training model is proposed in this paper. Based on Faster R-CNN, this paper uses guided filtering to remove fog, as well as combined with negative sample enhancement learning to train the model, thus solving recognition of ship in complex sea conditions. And with multi-scale training strategy, the multi-scale ship samples are produced and sent to the network for training, so as to solve the problem of small target recognition. The experimental results show that compared with the Faster R-CNN, the precision and recall of our method increase by 6.43% and by 4.68% respectively. It solves the difficulty of ships recognition under complex sea conditions and small ship recognition that cannot be solved by traditional deep learning methods, the trained model has good generalization ability and robustness.</p>
</abstract>

For the traditional deep learning cannot solve the fog, coastal background interference, and the difficulty of small ships recognition, a multi-scale deep learning training model is proposed in this paper. Based on Faster R-CNN, this paper uses guided filtering to remove fog, as well as combined with negative sample enhancement learning to train the model, thus solving recognition of ship in complex sea conditions. And with multi-scale training strategy, the multi-scale ship samples are produced and sent to the network for training, so as to solve the problem of small target recognition. The experimental results show that compared with the Faster R-CNN, the precision and recall of our method increase by 6.43% and by 4.68% respectively. It solves the difficulty of ships recognition under complex sea conditions and small ship recognition that cannot be solved by traditional deep learning methods, the trained model has good generalization ability and robustness.

Fine-grained Recognition of Ships Under Complex Sea Conditions

School of Computer Science and Engineering

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Fine-grained Recognition of Ships Under Complex Sea Conditions"}

For the traditional deep learning cannot solve the fog, coastal background interference, and the difficulty of small ships recognition, a multi-scale deep...

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

Training stage	Network	Number of iterations
1	RPN	40000
2	Fast RCNN	40000
3	RPN	80000
4	Fast RCNN	40000

Fine-grained Recognition of Ships Under Complex Sea Conditions

Published Online: May 26, 2023

Page range: 39 - 46

DOI: https://doi.org/10.2478/ijanmc-2022-0035

Keywords
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.

Figure 2.

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Figure 4.

Figure 5.

Figure 6.

Figure 7.

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Figure 9.

Figure 10.

Figure 11.

Comparison of recognition efficiency of the two algorithms

Faster R-CNN Training Process

Fine-grained Recognition of Ships Under Complex Sea Conditions

Published Online: May 26, 2023

Page range: 39 - 46

DOI: https://doi.org/10.2478/ijanmc-2022-0035

KeywordsShip 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.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

Comparison of recognition efficiency of the two algorithms

Faster R-CNN Training Process

Keywords
Ship Recognition, Complex Sea Conditions, Multi-scale, Fine-grained, Deep Learning