复杂背景下SAR图像近岸舰船目标检测

doi:10.12305/j.issn.1001-506X.2022.10.13

Abstract

Abstract:

In order to solve the problems of low detection rate, high false alarm rate and high missed detection rate of near-shore ship target detection from synthetic aperture radar (SAR) images caused by the vulnerability of SAR images to background clutter, a deformable feature fusion you only look once 5 (DFF-Yolov5) algorithm is proposed for the detection of near-shore ship targets in SAR images with complex background. The algorithm is based on the Yolov5 target detection algorithm, with two improvements in the feature extraction network: feature refinement and multi-feature fusion. A special data set for near-shore ship target ditection in complex background of SAR images is costructed. In the feature extraction network, a deformable convolutional neural network is used to change the position of the target sampling points to enhance the feature extraction capability of the target and improve the detection rate of SAR images ship targets in complex background. In the multi-feature fusion network structure, cascade and parallel pyramids are used to perform feature fusion at different levels. At the same time, cavity convolution is used to expand the visual field of feature extraction, enhance the adaptability of the network to near-shore multi-scale ship targets in complex background, and reduce the false alarm rate of SAR image ship target detection in complex backgrounds. Through the test experiments on the constructed complex background near-shore ship, the results show that the average accuracy of DFF-Yolov5 is 85.99%, compared with the original Yolov5, the average accuracy of the proposed method is improved by 5.09% and the precision is improved by 1.4%.

Key words: synthetic aperture radar (SAR), target detection, near-shore ship target, multi-feature fusion, deformable convolutional neural network

CLC Number:

TN95

Yonggang LI, Weigang ZHU, Qiongnan HUANG, Yuntao LI, Yonghua HE. Near-shore ship target detection with SAR images in complex background[J]. Systems Engineering and Electronics, 2022, 44(10): 3096-3103.

Figures/Tables 11

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References 21

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算法	图片尺寸	R/%	P/%	AP/%	FPS
Yolov5	680×680	62.27	91.78	80.90	43.48
D-Yolov5	680×680	62.77	91.46	81.03	40.00
DFF-Yolov5	680×680	69.60	93.20	85.99	31.25

参数	整幅图像	A1	E1	F1	G1	H1
大小	680×680	15×65	14×35	16×37	15×36	13×34
像素占比/%	-	0.21	0.11	0.13	0.17	0.096

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Near-shore ship target detection with SAR images in complex background

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