| 基于改进RA-CNN的舰船光电目标识别方法 |
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| 引用本文： | 霍煜豪,徐志京.基于改进RA-CNN的舰船光电目标识别方法[J].上海海事大学学报,2019,40(3):38-43. |
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| 作者姓名： | 霍煜豪 徐志京 |
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| 作者单位： | 上海海事大学信息工程学院 |
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| 基金项目： | 国家自然科学基金（61673259） |
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| 摘 要： | 针对光电图像中舰船分类检测困难的问题,提出一种基于改进循环注意卷积神经网络(recurrent attention convolutional neural network,RA-CNN)的舰船目标识别方法。该方法中的VGG19采用多个卷积层提取图像特征,注意建议网络(attention proposal network,APN)通过全连接层的输出定位特征区域,然后采用尺度依赖池化(scale-dependent pooling,SDP)算法选择VGG19中合适的卷积层输出进行类别判定,最后引入多特征描述特征区域,交叉训练VGG19和APN来加速收敛和提高模型精度。利用自建舰船数据集对方法进行测试,识别准确率较VGG19和RA-CNN有较大提升,识别准确率最高可达86.7%。
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| 关 键 词： | 舰船识别 细粒度图像分类 循环注意卷积神经网络(RA-CNN) 尺度依赖池化（SDP） 交叉训练 |
| 收稿时间： | 2018/9/3 0:00:00 |
| 修稿时间： | 2018/11/30 0:00:00 |
Photoelectric ship target identification method based on improved RA-CNN |
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| huoyuhao and xuzhijing.Photoelectric ship target identification method based on improved RA-CNN[J].Journal of Shanghai Maritime University,2019,40(3):38-43. |
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| Authors: | huoyuhao and xuzhijing |
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| Institution: | Shanghai Maritime University and Shanghai Maritime University |
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| Abstract: | Aiming at the difficulty of classification and detection of ships in photoelectric images, a ship target identification method based on the improved recurrent attention convolutional neural network (RA-CNN) is proposed. The VGG19 in the method uses multiple convolutional layers to extract image features. The attention proposal network (APN) locates the feature region through the output of the fully connected layer, and then uses the scale dependent pooling (SDP) algorithm to select the appropriate convolution in VGG19 for class determination. The multiple features are introduced to describe feature regions. The VGG19 and APN are cross trained to accelerate the convergence and improve the accuracy. The method is tested using the self built ship database of the model. The identification accuracy of the method is higher than that of VGG19 and RA CNN, and the highest identification accuracy is 86.7%. |
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