基于超密集特征金字塔网络的SAR图像舰船检测

doi:10.3969/j.issn.1001-506X.2020.10.09

Abstract

Abstract:

Aiming at the difficulty of ship target detection in space-borne synthetic aperture radar (SAR) images, a detection algorithm based on super dense feature pyramid networks is proposed. Firstly, residual neural network is used to extract features from original images and construct feature maps. Secondly, in order to enhance feature propagation and reuse, cross-scale feature layers are connected to obtain super dense feature pyramid and establish multi-scale high-level semantic feature mapping. Thirdly, candidate region is extracted from each layer of pdyramids by the regional proposal networks, and input into the detectim network. Finally, by fusing the candidate region and its surrounding contextual information to make the detector focus on the sea areas to suppress the false alarms, and provides supplementary information for the classifier to calculate confidence and bounding box regression. Simulation experiments show that the proposed network framework is reasonable and the detection performance is superior.

Key words: synthetic aperture radar (SAR), convolutional neural network (CNN), super dense feature pyramid networks, contextual information

CLC Number:

TP391

Zishuo HAN, Chunping WANG, Qiang FU, Yan XU. Ship detection in SAR images based on super dense feature pyramid networks[J]. Systems Engineering and Electronics, 2020, 42(10): 2214-2222.

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References 32

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数据集	星载SAR图像	子图像		所含目标数
数据集	星载SAR图像	含目标子图像	无目标子图像	所含目标数
训练集	5	1 936	-	4 284
测试集	2	591	2 945	1 232

β取值	N_{total_target}	N_td	N_fd	P_d/%	P_f/%	F₁
1	1 270	1 101	169	89.37	13.31	0.880
2	1 284	1 124	160	91.23	12.46	0.893
3	1 289	1 135	154	92.13	11.95	0.900
4	1 312	1 153	159	93.59	12.12	0.906
5	1 287	1 161	126	94.24	9.79	0.922
6	1 271	1 074	197	87.18	15.50	0.858
7	1 239	1 077	162	87.42	13.07	0.872

子测试集	舰船尺度范围	样本量	所含目标数	P_d/%	P_f/%	F₁
1	≥48²	90	197	92.89	10.29	0.913
2	24²~48²	220	462	95.02	9.67	0.926
3	≤24²	120	268	93.66	10.36	0.916

方法	N_{total_target}	N_td	N_fd	P_d/%	P_f/%	F₁	测试耗时/s
FPN	1 329	1 087	242	88.23	18.21	0.849	0.28
Faster R-CNN+	1 316	1 083	233	87.91	17.71	0.850	0.22
CMF-RCNN	1 272	1 075	197	87.26	15.49	0.859	0.22
SSD	1 345	1 124	221	91.23	16.43	0.872	0.20
SSD+	1 353	1 147	206	93.10	15.23	0.887	0.21
DFPN	1 275	1 116	159	90.58	12.47	0.890	0.30
本文算法	1 287	1 161	126	94.24	9.79	0.922	0.35

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Ship detection in SAR images based on super dense feature pyramid networks

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方法	目标真值数	检测出的目标总数	正确检测数	虚检数	漏检数
FPN	88	76	66	10	22
Faster R-CNN+	88	73	64	9	24
CMF-RCNN	88	70	62	8	26
SSD	88	85	76	9	12
SSD+	88	89	79	10	9
DFPN	88	84	77	7	11
本文算法	88	86	83	3	5