基于YOLO框架的无锚框SAR图像舰船目标检测

doi:10.12305/j.issn.1001-506X.2022.12.14

Abstract

Abstract:

For synthetic aperture radar (SAR) multi-target detection applications, this paper proposes an anchor free SAR image ship target detection method based on the you only look once (YOLO) framework. This method is aimed at the disadvantage that the YOLOv3 anchor needs to be preset and cannot fit perfectly. By adopting the anchor free method, it can better adapt to the size of the detected target and facilitate the use of multi-scale targets. On this basis, the attention mechanism is added to the CSPDarknet53 network as a feature extraction network, and then after an improved feature pyramid network (FPN) that can increases the receptive field, the feature map is transmitted to the anchor free detection head, which effectively improves the prediction precision of the target category and location. Experiments show that the improved algorithm has an average precision of 3.8% higher than YOLOv3 on the public SAR ship data set, reaching 94.8%, and the false alarm rate is reduced by 4.8%.

Key words: synthetic aperture radar (SAR) image, YOLO, anchor free, ship target detection

CLC Number:

TP751

Xiaoya JIA, Hongqiao WANG, Yadan YANG, Zhongma CUI, Bin XIONG. Anchor free SAR image ship target detection method based on the YOLO framework[J]. Systems Engineering and Electronics, 2022, 44(12): 3703-3709.

Figures/Tables 8

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References 38

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实验序号	Backbone	Neck	Head	AP/%	Recall/%	FA/%	大小/MB
1	Darknet53	YOLOv3Neck	YOLOv3Head	91.0	93.3	14.8	246.0
2	Darknet53	FPN	FoveaBox	94.7	96.0	16.6	208.6
3	CSPDarknet53	FPN	FoveaBox	94.4	95.6	14.5	152.7
4	CSPDarknet53	FPN+RFB	FoveaBox	93.8	95.0	12.8	155.7
5	CSPDarknet53+GC	FPN	FoveaBox	94.1	95.4	9.3	152.9
6	CSPDarknet53+GC	FPN+RFB	FoveaBox	94.8	96.0	10.0	155.9

网络	AP/%	Recall/%	FA/%	检测速度task/s
YOLOv3	91.0	93.3	14.8	30.1
FoveaBox	91.2	95.0	46.7	30.5
本文方法	94.8	96.0	10.0	23.7

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Anchor free SAR image ship target detection method based on the YOLO framework

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