[1]樊海玮,史 双,蔺 琪,等.复杂背景下 SAR 图像船舶目标检测算法研究[J].计算机技术与发展,2021,31(10):49-55.[doi:10. 3969 / j. issn. 1673-629X. 2021. 10. 009]
　FAN Hai-wei,SHI Shuang,LIN Qi,et al.Research on Ship Target Detection Algorithm in Complex Background SAR Image[J].,2021,31(10):49-55.[doi:10. 3969 / j. issn. 1673-629X. 2021. 10. 009]
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复杂背景下 SAR 图像船舶目标检测算法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年10期
页码:
49-55
栏目:
图形与图像
出版日期:
2021-10-10

文章信息/Info

Title:
Research on Ship Target Detection Algorithm in Complex Background SAR Image
文章编号:
1673-629X(2021)10-0049-07
作者:
樊海玮; 史 双; 蔺 琪; 孙 欢; 秦佳杰
长安大学 信息工程学院,陕西 西安 710064
Author(s):
FAN Hai-wei; SHI Shuang; LIN Qi; SUN Huan; QIN Jia-jie
School of Information Engineering,Chang’an University,Xi’an 710064,China
关键词:
深度学习; 目标检测; SAR 图像; 双注意力机制; 特征融合
Keywords:
deep learning; target detection; SAR images; dual attention mechanism; feature fusion
分类号:
TP312
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 10. 009
摘要:
针对复杂背景 SAR 图像船舶目标检测易受地物干扰影响,导致模型检测率低的问题,提出将结合通道和空间的双注意力机制 CBAM 引入目标检测网络;将膨胀卷积和 concat 特征融合技术应用于目标检测网络中提升模型对小尺寸目标的鲁棒性;为进一步提高模型的检测速度,使用轻量级 MobileNet 作为基础特征提取网络;同时采用一个新的二分类损失函数使模型训练能够对难易样本设置不同的权重。 最后,通过在构建的复杂背景 SAR 图像船 舶目标检测数据集 SDATA上进行实验, 实验结果表明该算法在复杂背景 SAR 船舶目标检测中其平均检测精度与综合评价指标 F1 -score 值分别为88. 9% 和 91. 2% ,检测速度达 42. 1 fps,从而验证了该模型不仅能够有效提升复杂背景 SAR 图像船舶目标的检测精度,而且在一定程度上提高了目标的检测速度。
Abstract:
Aiming at the problem that ship detection in complex background SAR images is easily affected by ground objects,which leads to low detection rate of model, we propose to introduce CBAM,which combines channel and space mechanism,into target detection network. The expansion convolution and concatenation feature fusion technology are applied to the target detection network to improve the robustness of model to small targets. In order to further improve the detection speed of model,the lightweight Mobile Net is used,and a new two class loss function is used to make the model training set different weights for difficult and easy samples. Finally,experiments are carried out on SDATA,a ship target detection data set with complex background SAR images. It is showed that the mean average precision and comprehensive evaluation index F1 -score of the proposed algorithm are 88. 9% and 91. 2% respectively,and the detection speed is 42. 1 fps. It is verified that the proposed model can not only effectively improve the detection accuracy of ship targets in SAR images with complex background,but also improve the detection speed to? ?a certain extent.

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更新日期/Last Update: 2021-10-10