基于特征增强的SAR图像舰船小目标检测算法
作者:
作者单位:

西北师范大学

作者简介:

通讯作者:

中图分类号:

TP751.1

基金项目:

国家自然科学基金项目(61861041)


A Ship Small Target Detection Algorithm Based on Feature Enhancement in SAR Image
Author:
Affiliation:

Northwest Normal University

Fund Project:

The National Natural Science Foundation of China (61861041)

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    摘要:

    针对合成孔径雷达(SAR)图像中小目标舰船检测困难的问题,提出基于单次多盒检测器的一种特征增强小目标检测算法。该算法提出一种混合多特征提取模块,该模块采用并行的普通卷积、不同空洞率的空洞卷积以及非对称卷积形成与舰船目标相匹配的感受野,以提高浅层网络对复杂形状小目标的特征提取能力。然后提出一种邻近多特征融合模块,将特征信息进行更科学的深层次融合,对小目标特征进一步增强。最后根据SAR图像单通道的特性,缩减特征提取网络VGG-16的冗余特征通道。在公开的SSDD数据集上与其他检测算法进行对比试验,实验结果表明,所提方法将平均精确度提升至93.44%,检测速度提升至为41.8FPS,而参数量减少为18.74M,综合性能优于其他检测算法。

    Abstract:

    To solve the problem of small target ship detection in synthetic aperture radar (SAR) image, a feature enhanced small target detection algorithm based on Single Shot Multi box Detector is proposed. The algorithm proposes a hybrid feature extraction module, which uses general convolution, dilated convolution with different dilation ratio and asymmetric convolution to form a receptive field matching the ship target, so as to improve the feature extraction ability of shallow network for small targets with complex shape.Then, a neighbor feature fusion module is proposed, which integrates the feature information more scientifically and deeply, and further enhances the features of small targets. Finally, according to the characteristics of single channel of SAR image, redundant feature channels of feature extraction network VGG-16 are reduced. It is compared with other detection algorithms on the public SSDD data set. The experimental results show that the proposed method improves the average accuracy to 93.44%, the detection speed is improved to 41.8FPS, and the number of parameters is reduced to 18.74M, which is superior to other detection algorithms in comprehensive performance.

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历史
  • 收稿日期:2021-04-02
  • 最后修改日期:2021-09-26
  • 录用日期:2021-09-28
  • 在线发布日期: 2021-10-01
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