| Autor: |
XU Zhijing, SUN Jiuwu, HUO Yuhao |
| Předmět: |
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| Zdroj: |
Journal of Computer Engineering & Applications; 5/15/2022, Vol. 58 Issue 10, p224-230, 7p |
| Abstrakt: |
In order to solve the problem of low recognition accuracy in fine-grained ship images with a single feature, a ship target recognition method based on the fusion of recurrent attention convolutional neural network (RA-CNN) and multi- feature regions is proposed. This method introduces the scale-dependent pooling (SDP) algorithm in the VGG-19 network to solve the problem of excessive pooling of small targets in the network, and improves the recognition performance of small ships. The attention proposal network (APN) introduces joint clustering algorithm to form multiple independent feature regions, so that the whole model can make full use of global information and improve the accuracy of ship recognition. At the same time, a feature region optimization method is designed to reduce the overlap rate of multiple feature region and solve the problem of overfitting. By defining a new loss function to crosstrain the VGG-19 and APN, the convergence is accelerated. The proposed method is tested by using the open optoelectronic ship database, the recognition accuracy is up to 90.2%. Both the recognition rate and the robustness of the model are greatly improved compared with the single feature. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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