基于特征增强网络的SAR图像舰船目标检测

doi:10.12305/j.issn.1001-506X.2023.04.12

Abstract

Abstract:

Traditional detection methods are inefficient and have a high probability of false alarm due to the high complexity of synthetic aperture radar (SAR) image scenes and small scale of ship targets. To address these problems, this paper proposes a feature-enhanced network for SAR image ship target detection is proposed. Firstly, feature information is extracted using I-Darknet53 (improved Darknet-53), and a four-layer feature pyramid is constructed to enrich low-level features. Secondly, multiple feature layers are connected across scales to make low-level detail information easier to map to high-level semantic information, thus enhancing the propagation and reuse of features. Finally, the feature information is enhanced using a multi-scale attention model to provide a high-quality judgment basis for the detector. The experimental results show that the average detection accuracy of the proposed algorithm on the SSDD dataset is 95%. The proposed algorithm has high precision compared with other network models.

Key words: synthetic aperture radar (SAR) image, target detection, feature enhancement, multi-scale fusion, multi-scale attention

CLC Number:

TP391

Dongdong ZHANG, Chunping WANG, Qiang FU. Ship target detection in SAR image based on feature-enhanced network[J]. Systems Engineering and Electronics, 2023, 45(4): 1032-1039.

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References 24

1	刘洁瑜, 赵彤, 刘敏. 基于RetinaNet的SAR图像舰船目标检测[J]. 湖南大学学报(自然科学版), 2020, 47 (2): 85- 91.
	LIU J Y , ZHAO T , LIU M . Ship target detection in SAR image based on RetinaNet[J]. Journal of Hunan University (Natural Science Edition), 2020, 47 (2): 85- 91.
2	韩子硕, 王春平, 付强, 等. 基于超密集特征金字塔网络的SAR图像舰船检测[J]. 系统工程与电子技术, 2020, 42 (10): 2214- 2222. doi: 10.3969/j.issn.1001-506X.2020.10.09
	HAN Z S , WANG C P , FU Q , et al. Ship detection in SAR images based on super dense feature pyramid networks[J]. Systems Engineering and Electronics, 2020, 42 (10): 2214- 2222. doi: 10.3969/j.issn.1001-506X.2020.10.09
3	WANG C L , BI F K , ZHANG W P , et al. An intensity-space domain CFAR method for ship detection in HR SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14 (4): 529- 533. doi: 10.1109/LGRS.2017.2654450
4	ZHAO Z , JI K F , XING X W , et al. Ship surveillance by integration of space-borne SAR and AIS-review of current research[J]. Journal of Navigation, 2014, 67 (1): 177- 189. doi: 10.1017/S0373463313000659
5	FINGAS M F , BROWN C E . Review of ship detection from airborne platforms[J]. Canadian Journal of Remote Sensing, 2001, 27 (4): 379- 385. doi: 10.1080/07038992.2001.10854880
6	KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks[J]. Artificial Neural Network, 2017, 60 (6): 84- 90.
7	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.
8	GIRSHICK R. Fast R-CNN[C]//Proc. of the IEEE International Conference on Computer Vision, 2015: 1440-1448.
9	REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]//Proc. of the 28th International Conference on Neural Information Processing Systems, 2015: 91-99.
10	HE K, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]//Proc. of the IEEE International Conference on Computer Vision, 2017: 2980-2988.
11	LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]//Proc. of the European Conference on Computer Vision, 2016: 21-37.
12	LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proc. of the IEEE Trans. on Pattern Analysis and Machine Intelligence, 2017: 318-327.
13	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.
14	REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 6517-6525.
15	REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. [2021-04-10]. https://arxiv.org/abs/1804.02767.
16	LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 936-944.
17	SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[C]//Proc. of the 31st AAAI Conference on Artificial Intelligence, 2017: 4278-4284.
18	LI J W, QU C W, SHAO J Q. Ship detection in SAR images based on an improved faster R-CNN[C]//Proc. of the SAR in Big Data Era: Models, Methods and Applications, 2017.
19	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
20	XIE S, GIRSHICK R, DOLLÁR P, et al. Aggregated residual transformations for deep neural networks[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 5987-5995.
21	HUANG G, LIU Z, VAN D M L, et al. Densely connected convolutional networks[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2261-2269.
22	LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 8759-8768.
23	刘杰平, 温竣文, 梁亚玲. 基于多尺度注意力导向网络的单目图像深度估计[J]. 华南理工大学学报(自然科学版), 2020, 48 (12): 52- 62.
	LIU J P , WEN J W , LIANG Y L . Monocular image depth estimation based on multi-scale attention oriented networl[J]. Journal of South China University of Technology (Natural Science Edition), 2020, 48 (12): 52- 62.
24	刘元宁, 吴迪, 朱晓冬, 等. 基于YOLOv3改进的用户界面组件检测算法[J]. 吉林大学学报(工学版), 2021, 51 (3): 1026- 1033.
	LIU Y N , WU D , ZHU X D , et al. User interface components detection algorithm based on improved YOLOv3[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51 (3): 1026- 1033.

数据集	锚框
数据集	1	2	3	4	5	6	7	8	9	10	11	12
SSDD	9, 11	11, 23	19, 13	15, 29	30, 17	22, 50	30, 28	36, 65	52, 24	55, 72	59, 118	123, 39
SSDD	9, 12	12, 24	17, 12	18, 40	27, 17	30, 61	48, 24	59, 113	103, 44	-	-	-

数据集	图像数量	舰船数量
训练集	928	2 061
测试集	232	479

方法	SSDD
方法	TP	FP	FN	P	R	AP
YOLOv3	438	37	41	0.922 1	0.914 4	0.864 1
YOLOv3+I-Darknet-53	442	50	37	0.936 4	0.922 7	0.927 6
YOLOv3+4Layers	456	52	23	0.897 6	0.952 0	0.932 8
YOLOv3+MFPN	443	32	36	0.932 6	0.924 8	0.935 1
YOLOv3+MSA	450	34	29	0.929 6	0.939 5	0.902 3
YOLO-MDM	453	33	26	0.932 1	0.945 7	0.950 2

方法	主干网络	SSDD
方法	主干网络	P	R	AP
YOLOv3	Darknet-53	0.922 1	0.914 4	0.864 1
YOLOv4	Darknet-53	0.884 2	0.975 3	0.971 1
Faster R-CNN	ResNet-50	0.729 0	0.814 2	0.772 3
SSD	Mobilenet-v1	0.980 8	0.525 9	0.524 5
YOLO-MDM	I-Darknet-53	0.932 1	0.945 7	0.950 2

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Ship target detection in SAR image based on feature-enhanced network

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