基于改进型YOLOv3的SAR图像舰船目标检测

doi:10.12305/j.issn.1001-506X.2021.04.10

Abstract

Abstract:

Traditional synthetic aperture radar(SAR) image target detection methods rely on manual design features and are vulnerable to complex background interference, and their generalization ability is poor. The deep learning method can automatically extract features and has good anti-jamming characteristics, which is of great significance for future radar intelligent perception. Different from other conventional neural networks that can only detect fixed areas, an improved YOLOv3 SAR image ship detection method is proposed in this paper. The new YOLOv3 model is designed based on deforming convolution of adaptive sampling of the ship size and the shape, the ResNet50 variant feature extractor and the ShuffleNetv2 lightweight idea. Through SSDD dataset verification, compared with the original YOLOv3 model, the average accuracy increases from 93.21% to 96.94%, and the detection probability increases from 95.51% to 97.75%. In terms of the model size, the lightweight design model is only one-eighth of the original YOLOv3 model, which can be embedded for use.

Key words: synthetic aperture radar (SAR), ship detection, YOLOv3, deformable convolution, convolutional neural network

CLC Number:

TP751

Dong CHEN, Yanwei JU. Ship detection in SAR image based on improved YOLOv3[J]. Systems Engineering and Electronics, 2021, 43(4): 937-943.

Figures/Tables 10

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References 29

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模型	GT	TP	FN	FP
Darknet53	267	255	12	32
Shufflenetv2	267	257	10	29
ResNet50	267	259	8	38
ResNet50-d	267	258	9	31
ResNet50-d-DCN	267	261	6	29

模型	P_d	P_MA	P_LA	Precision	mAP	大小/MB
Darknet53	95.51	4.49	11.15	88.85	93.21	234
Shufflenetv2	96.25	3.75	10.14	89.86	92.84	27
ResNet50	97.00	3.00	12.79	87.21	95.02	172
ResNet50-d	96.63	3.37	10.73	89.27	95.23	172
ResNet50-d-DCN	97.75	2.25	10.00	90.00	96.64	173

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Ship detection in SAR image based on improved YOLOv3

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