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收稿日期：2020-06-05，
修回日期：2020-08-11，
录用日期：2020-8-18，
纸质出版日期：2021-05-16
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目的
2
船舶在合成孔径雷达（synthetic aperture radar，SAR）图像中的检测是研究热点，但目前适合近岸舰船检测的方法并不多。在SAR图像中，近岸舰船受到岸上建筑物的干扰严重，尤其是对于排列紧密的近岸船舶来说，其对比度相似，很难区分船舶与背景。为解决近岸舰船检测困难问题，提出了一种基于加权双向注意金字塔网络的近岸舰船检测方法。
方法
2
本文在FCOS（fully convolutional one-stage）网络的基础上提出了一种新的双向特征金字塔网络。将卷积注意力机制模块（convolutional block attention module，CBAM）与金字塔网络的每个特征图进行连接，提取丰富的语义信息特征；借鉴PANet（path aggregation network）的思想，添加自下而上的金字塔模块，突出不同尺度船舶的显著特征。最后提出了一种加权特征融合方式，使特征图提取的特征信息的着重点不同，提高舰船检测精度。
结果
2
本文在公开的SAR图像舰船数据集SSDD（SAR ship detection dataset）上进行实验。实验结果表明，相比原FCOS方法，本文方法的检测精度提高了9.5%；与对比方法相比，本文方法在同等条件下的检测精度达到90.2%。在速度方面，本文方法比SSD提高0.6 s，比Faster R-CNN（region convolutional neural network）提高1.67 s，明显优于对比方法。
结论
2
本文通过改进特征网络和特征融合方式，提高了算法对SAR图像舰船目标检测中背景复杂、排列紧密的近岸舰船目标的定位效果，有效增强了对舰船目标定位的准确性。
Objective
2
Synthetic aperture radar (SAR) is an active sensor that uses microwave remote sensing technology. Compared with visible and infrared sensors
it is not limited by light
weather
and climate conditions and has all-weather and multi-angle data acquisition capability. With the development of SAR imaging technology
SAR image has been widely used in the military field for intelligence detection
navigation guidance
ocean ship detection
etc. Through the detection of ship target in the SAR image
the ship information of the sea surface
port
and other locations can be obtained quickly
which improves border prevention ability. The traditional methods of SAR ship detection have difficulty in detecting small-scale ships and avoiding the interference of inshore complex background. Moreover
the quality of SAR images needs to be high
and the images need to be preprocessed before detection. In addition
the robustness and generalization of most SAR images are not good enough for specific scenes
and they are susceptible to speckle noise
which has certain limitations. With the development of artificial intelligence
machine learning has been introduced into the SAR image target detection field. Deep learning is an enhanced version of machine learning. Recently
it has been gradually applied for ship detection in SAR images
but some problems need to be addressed. First
the near shore ships are seriously disturbed by the buildings on the shore. The existing detection methods cannot effectively distinguish the ship target from the background
so it is easy to mix the background with the ship target or mistake the target for the background
resulting in missed detection. Second
the existing algorithm cannot accurately locate the closely arranged ship targets
and the positioning effect is poor. It is easy to regard multiple targets as one target
which leads to wrong detection
resulting in low accuracy of detection results or high constant false alarm rate. To solve these problems
this study proposes a detection method based on bidirectional attention feature pyramid network.
Method
2
Experiments are conducted on the SAR ship detection dataset (SSDD) of the public SAR image ship dataset. This dataset comes from the Naval Aviation University
which is a common dataset in this field in China. Affine
fuzzy
and noisy data enhancement operations are performed to improve the generalization ability of the training model. A new bidirectional feature pyramid network is proposed based on the original fully convolutional one-stage (FCOS) object detection network. By connecting the attention mechanism module with each feature graph of the pyramid network
the features of a large amount of semantic information can be extracted. At the same time
we use convolutional block attention module (CBAM) to refine the stitched feature map and combine the idea of path aggregation network for instance segmentation. The bottom-up pyramid module is added to further highlight the prominent features of targets at different scales
so as to improve the ability of the network to accurately locate ship targets in a complex background. Then
a weighted feature fusion method is proposed
which makes the feature information extracted from different feature maps to have different emphases and then combines the salient features with the global non-fuzzy features to improve the ship detection accuracy. Finally
the fused feature map is fed back to the detection network to obtain the final detection result. In addition
a NVIDIA Titan RTX device is used for the experimental platform in this study
and the operating system is Ubuntu 18.04. The batch size of the training is set to the initialization model
the batch size of the training is 8
the total iteration number is 50 000
the initial learning rate is 0.001
the learning rate attenuation coefficient is 0.000 1
the 2 000 attenuation is once
and the kinetic parameters are analyzed at 0.9 mm.
Result
2
We compared the added modules and found that the added modules have different degrees of improvement compared with the original FCOS method. At the same time
we also compared the experimental results with three other existing models
including SSD
Faster R-CNN (region convolutional neural network)
and original FCOS. Experimental results show that the detection accuracy of the proposed algorithm is 9.5% higher than that of the original FCOS method. The detection accuracy of this algorithm reached 90.2% under the same conditions
which was 14.09% higher than that of SSD and 8.5% higher than that of Faster R-CNN. The results show that the model algorithm can obtain accurate results. In addition
in terms of time
the proposed algorithm is 5 ms slower than FCOS. However
the detection speed was 1.6 times higher than that of SSD and 6 times higher than that of Faster R-CNN. In terms of speed
the detection speed of the proposed algorithm is obviously faster than that of other existing methods. From the comparison of experimental results
the proposed algorithm is not only good for the detection of small target ships in deep sea
but also good for the detection of near shore ship targets in the complex background
especially for closely arranged ship targets
with high accuracy of positioning effect.
Conclusion
2
By improving the network features and the feature fusion method
the ship detection algorithm in SAR images is improved on the premise of ensuring the same detection effect of deep-sea ships. Under complex background
the ship positioning effect is compact
and the accuracy of ship detection is effectively enhanced
which is superior to other ship detection methods based on SSDD.
An Q Z, Pan Z X and You H J. 2018. Ship detection in Gaofen-3 SAR images based on sea clutter distribution analysis and deep convolutional neural network. Sensors, 18(2): #334[DOI:10.3390/s18020334]
Girshick R, Donahue J, Darrell T and Malik J. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE: 580-587[ DOI: 10.1109/CVPR.2014.81 http://dx.doi.org/10.1109/CVPR.2014.81 ]
Girshick R. 2015. Fast R-CNN//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE: 1440-1448[ DOI: 10.1109/ICCV.2015.169 http://dx.doi.org/10.1109/ICCV.2015.169 ]
Hu C H, Chen C, He C, Pei H and Zhang J X. 2019. SAR detection for small target ship based on deep convolutional neural network. Journal of Chinese Inertial Technology, 27(3): 397-405, 414
胡昌华, 陈辰, 何川, 裴洪, 张建勋. 2019. 基于深度卷积神经网络的SAR图像舰船小目标检测. 中国惯性技术学报, 27(3): 397-405, 414[DOI:10.13695/j.cnki.12-1222/o3.2019.03.018]
Kang M, Leng X G, Lin Z and Ji K F. 2017. A modified faster R-CNN based on CFAR algorithm for SAR ship detection//Proceedings of 2017 International Workshop on Remote Sensing with Intelligent Processing. Shanghai, China: IEEE: 1-4[ DOI: 10.1109/RSIP.2017.7958815 http://dx.doi.org/10.1109/RSIP.2017.7958815 ]
Krizhevsky A, Sutskever I and Hinton G E. 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6): 84-90[DOI:10.1145/3065386]
Leng X G, Ji K F, Yang K and Zou H X. 2015. A bilateral CFAR algorithm for ship detection in SAR images. IEEE Geoscience and Remote Sensing Letters, 12(7): 1536-1540[DOI:10.1109/lgrs.2015.2412174]
Li J W, Qu C W and Peng S J. 2019. A joint SAR ship detection and azimuth estimation method. Geomatics and Information Science of Wuhan University, 44(6): 901-907
李健伟, 曲长文, 彭书娟. 2019. SAR图像舰船目标联合检测与方向估计. 武汉大学学报(信息科学版), 44(6): 901-907[DOI:10.13203/j.whugis20170328]
Li J W, Qu C W and Shao J Q. 2017. Ship detection in SAR images based on an improved faster R-CNN//Proceedings of 2017 SAR in Big Data Era: Models, Methods and Applications. Beijing, China: IEEE: 1-6[ DOI: 10.1109/BIGSARDATA.2017.8124934 http://dx.doi.org/10.1109/BIGSARDATA.2017.8124934 ]
Liu S, Qi L, Qin H F, Shi J P and Jia J Y. 2018. Path aggregation network for instance segmentation//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 8759-8768[ DOI: 10.1109/CVPR.2018.00913 http://dx.doi.org/10.1109/CVPR.2018.00913 ]
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y and Berg A C. 2016. SSD: single shot MultiBox detector//Proceedings of the 14th European Conference on Computer Vision. Cham: Springer: 21-37[ DOI: 10.1007/978-3-319-46448-0_2 http://dx.doi.org/10.1007/978-3-319-46448-0_2 ]
Redmon J, Divvala S, Girshick R and Farhadi A. 2016. You only look once: unified, real-time object detection//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 779-788[ DOI: 10.1109/CVPR.2016.91 http://dx.doi.org/10.1109/CVPR.2016.91 ]
Ren S Q, He K M, Girshick R and Sun J. 2017. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6): 1137-1149[DOI:10.1109/TPAMI.2016.2577031]
Shelhamer E, Long J and Darrell T. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4): 640-651[DOI:10.1109/TPAMI.2016.2572683]
Tian Z, Shen C H, Chen H and He T. 2019. FCOS: fully convolutional one-stage object detection//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 9626-9635[ DOI: 10.1109/ICCV.2019.00972 http://dx.doi.org/10.1109/ICCV.2019.00972 ]
Woo S, Park J, Lee J Y and Kweon I S. 2018. CBAM: convolutional block attention module//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany: Springer: 3-19[ DOI: 10.1007/978-3-030-01234-2_1 http://dx.doi.org/10.1007/978-3-030-01234-2_1 ]
Yu D H, Zhang B M, Guo H T, Zhao C and Xu J F. 2018. Joint salient feature and convolutional neural network for ship detection in remote sensing images. Journal of Image and Graphics, 23(12): 1947-1958
余东行, 张保明, 郭海涛, 赵传, 徐俊峰. 2018. 联合显著性特征与卷积神经网络的遥感影像舰船检测. 中国图象图形学报, 23(12): 1947-1958[DOI:10.11834/jig.180323]
Zhang L, Hong X, Wang Y H and Zhou B. 2018. Inshore ship detection in high-resolution remote sensing image using projection analysis. Journal of Image and Graphics, 23(9): 1424-1432
张磊, 洪星, 王岳环, 周斌. 2018. 高分辨率遥感图像投影分析的靠岸舰船检测. 中国图象图形学报, 23(9): 1424-1432[DOI:10.11834/jig.170612]
Zhou Q, Qu C W, Li J W and Yao L B. 2017. Extracting small harbor areas in large-scene SAR images. Journal of Image and Graphics, 22(8): 1128-1134
周强, 曲长文, 李健伟, 姚力波. 2017. 大场景SAR图像中提取小型港口区域技术研究. 中国图象图形学报, 22(8): 1128-1134[DOI:10.11834/jig.170008]
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