为实现不同角度和不同距离下，船舶采集图像的智能分类，提出基于多尺度注意力深度卷积神经网络分类算法的船舶采集图像智能分类。将采集的船舶图像输入该网络中，网络的多尺度深度卷积层采用3个多尺度特征注意力模块结合深度残差模块，提取船舶采集图像不同层次的局部不变性特征；池化层对该特征转换处理后形成特征向量；全连接层池化层引入尺寸匹配函数融合特征向量，形成多尺度纹理特征向量并输入分类层，实现船舶采集图像智能分类。测试结果显示：该方法可实现不同船舶类别图像特征提取，gini指数结果均在0.963以上，可依据分类需求，实现不同角度以及距离条件下、不同的船舶图像类别的准确分类。
In order to realize the intelligent classification of ship image acquisition under different angles and different distances, an intelligent classification of ship image acquisition based on multi-scale attention deep convolutional neural network classification algorithm is proposed. The collected ship images were input into the network, and the multi-scale deep convolution layer of the network used three multi-scale feature attention modules combined with the depth residual module to extract the local invariance features at different levels of the ship image acquisition. The pooling layer transforms the feature and forms the feature vector. In the pooling layer of the fully connected layer, the size matching function is introduced to fuse the feature vectors, and the multi-scale texture feature vectors are formed and input into the classification layer to realize the intelligent classification of ship image acquisition. The test results show that the proposed method can achieve image feature extraction of different ship categories, and the gini index results are all above 0.963. According to the classification requirements, the method can achieve accurate classification of different ship image categories under different angles and distances.
2022,44(22): 158-161 收稿日期：2022-08-16
DOI：10.3404/j.issn.1672-7649.2022.22.031
分类号：TP391
作者简介：苑靖国(1983-),男,硕士,讲师,从事航海专业教育研究
参考文献：
[1] 任永梅, 杨杰, 郭志强, 等. 基于多尺度卷积神经网络的自适应熵加权决策融合船舶图像分类方法[J]. 电子与信息学报, 2021, 43(5): 1424–1431
REN Yongmei, YANG Jie, GUO Zhiqiang, et al. Self-adaptive entropy weighted decision fusion method for ship image classification based on multi-scale convolutional neural network[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1424–1431
[2] 徐频捷, 王诲喆, 李策, 等. 基于脉冲神经网络与移动GPU计算的图像分类算法研究与实现[J]. 计算机工程与科学, 2020, 42(3): 397–403
XU Pinjie, WANG Huizhe, LI Ce, et al. Image classification algorithm based on spiking neural network and mobile GPU computing[J]. Computer Engineering and Science, 2020, 42(3): 397–403
[3] 杨晶东, 朱锦图, 孙新博, 等. 基于动态衰减EMA的图像分类算法研究[J]. 小型微型计算机系统, 2020, 41(7): 1524–1529
YANG Jingdong, ZHU Jintu, SUN Xinbo, et al. Research on image classification algorithm based on dynamic decay EMA[J]. Journal of Chinese Computer Systems, 2020, 41(7): 1524–1529
[4] 顾广华, 曹宇尧, 李刚, 等. 基于语义标签生成和偏序结构的图像层级分类[J]. 软件学报, 2020, 31(2): 531–543
GU GuangHua, CAO YuYao, LI Gang, et al. Image hierarchical classification based on semantic label generation and partial order structure[J]. Journal of Software, 2020, 31(2): 531–543
[5] 张冀, 曹艺, 王亚茹, 等. 融合VAE和StackGAN的零样本图像分类方法[J]. 智能系统学报, 2022, 17(3): 593–601
ZHANG Ji, CAO Yi, WANG Yaru, et al. Zero-shot image classification method combining VAE and StackGAN[J]. CAAI Transactions on Intelligent Systems, 2022, 17(3): 593–601
[6] 朱海琦, 李宏, 李定文, 等. 基于生成对抗网络的单图像超分辨率重建[J]. 吉林大学学报(理学版), 2021, 59(6): 1491–1498
ZHU Haiqi, LI Hong, LI Dingwen, et al. Single image super-resolution reconstruction based on generative adversarial network[J]. Journal of Jilin University(Science Edition), 2021, 59(6): 1491–1498
[7] 丁国绅, 乔延利, 易维宁, 等. 基于光谱图像空间的改进SIFT特征提取与匹配[J]. 北京理工大学学报, 2022, 42(2): 192–199
DING Guoshen, QIAO Yanli, YI Weining, et al. Improved SIFT feature extraction and matching based on spectral image space[J]. Transactions of Beijing Institute of Technology, 2022, 42(2): 192–199
