SAR图像舰船目标检测的轻量化和特征增强研究

doi:10.19665/j.issn1001-2400.20230407

Abstract

Abstract:

The accuracy of ship targets detection in sythetic aperture radar images is susceptible to the nearshore clutter.The existing detection algorithms are highly complex and difficult to deploy on embedded devices.Due to these problems a lightweight and high-precision SAR image ship target detection algorithm CA-Shuffle-YOLO(Coordinate Shuffle You Only Look Once) is proposed in this article.Based on the YOLO v5 target detection algorithm,the backbone network is improved in two aspects:lightweight and feature refinement.The lightweight module is introduced to reduce the computational complexity of the network and improve the reasoning speed,and a collaborative attention mechanism module is introduced to enhance the algorithm's ability to extract the detailed information on near-shore ship targets.In the feature fusion network,weighted feature fusion and cross-module fusion are used to enhance the ability of the model to fuse the detailed information on SAR ship targets.At the same time,the depth separable convolution is used to reduce the computational complexity and improve the real-time performance.Through the test and comparison experiments on the SSDD ship target detection dataset,the results show that the detection accuracy of CA-Shuffle-YOLO is 97.4%,the detection frame rate is 206FPS,and the required computational complexity is 6.1GFlops.Compare to the original YOLO v5,the FPS of our algorithm is 60FPS higher with the required computational complexity of our algorithm being only the 12% that of the ordinary YOLOv5.

Key words: synthetic aperture radar, object detection, convolutional neural networks, feature extraction

CLC Number:

TP391.4

GONG Junyang, FU Weihong, FANG Houzhang. Research on lightweight and feature enhancement of SAR image ship targets detection[J].Journal of Xidian University, 2024, 51(2): 96-106.

Figures/Tables 15

References 18

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传感器类型或环境	成像极化方式	船舶情况	成像规模
RadarSat-2 TerraSAR-X Sentinel-1	HH,VV, VH,HV	1∶1 1∶2 2∶1	不同尺寸及材料
海洋环境	分辨率	成像位置
恶劣天气和晴朗天气	1 m~5 m	远海及近岸

骨干网络/指标	P	R	F1	mAP@0.5	mAP@0.5:0.95
CSPDarkNet	0.972	0.969	0.970	0.981	0.748
Shuffle-YOLO	0.968	0.934	0.950	0.969	0.704
Shuffle-YOLO+CA(1)	0.959	0.934	0.946	0.962	0.697
Shuffle-YOLO+CA(2)	0.952	0.945	0.948	0.967	0.708
Shuffle-YOLO+CA(3)	0.97	0.938	0.954	0.967	0.705
Shuffle-YOLO+CA(4)	0.955	0.94	0.947	0.965	0.697
Shuffle-YOLO+CA(5)	0.955	0.929	0.941	0.960	0.693
Shuffle-YOLO+CA(6)	0.975	0.943	0.959	0.971	0.705

骨干网络\指标	帧率	GFLOPs	参数量	F1_density[%/M-params]
CSPDarkNet	146	50.2	21 037 638	0.046
Shuffle-YOLO	221	4.3	2 024 166	0.469
Shuffle-YOLO+CA(1)	280	4.3	2 022 846	0.468
Shuffle-YOLO+CA(2)	283	4.4	2 049 126	0.463
Shuffle-YOLO+CA(3)	267	4.4	2 049 126	0.466
Shuffle-YOLO+CA(4)	259	4.5	2 132 550	0.444
Shuffle-YOLO+CA(5)	231	4.4	2 026 014	0.464
Shuffle-YOLO+CA(6)	299	4.8	2 465 286	0.389

模型/指标	P	R	F1	mAP@0.5	mAP@0.5:0.95
CSPDarknet+PANet	0.972	0.969	0.970	0.981	0.748
CSPDarknet+BiFPN	0.981	0.965	0.973	0.982	0.747
CSPDarknet+WM-FPN	0.978	0.971	0.974	0.982	0.754

模型/指标	帧率	GFLOPs	参数量	F1_density[%/M-params]
CSPDarknet+PANet	146	50.2	21 037 638	0.046
CSPDarknet+BiFPN	152	52.1	21 523 110	0.045
CSPDarknet+WM-FPN	160	48.5	19 182 687	0.051

Research on lightweight and feature enhancement of SAR image ship targets detection

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Shuffle-YOLO	CA(6)	BiFPN	WM-FPN	mAP@0.5	mAP@0.5:0.95	帧率	GFLOPs
				0.981	0.748	146	50.2
√				0.969	0.704	221	4.3
√	√			0.971	0.705	299	4.8
		√		0.982	0.747	152	52.1
			√	0.982	0.754	160	48.5
√		√		0.963	0.711	245	5.6
√			√	0.965	0.711	223	5.0
√	√	√		0.966	0.705	224	6.1
√	√		√	0.974	0.716	206	6.1