Improved YOLOv4 Based on Attention Mechanism for Ship Detection in SAR Images

doi:10.1109/access.2022.3154474

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	Improved YOLOv4 Based on Attention Mechanism for Ship Detection in SAR Images
	Y. L. Gao; Z. Y. Wu; M. Ren and C. A. Wu
	2022
发表期刊	Ieee Access
ISSN	2169-3536
卷号	10 页码:23785-23797
摘要	Ship detection in synthetic aperture radar (SAR) images is an important and challenging work in the field of image processing. Traditional detection algorithms usually rely on handmade features or predefined thresholds, the different performance is obtained with varying degrees of prior knowledge, and it is difficult to take advantage of big data. Recently, deep learning algorithms have found wide applications in ship detection from SAR images. However, due to the complex backgrounds and multiscale ships, it is hard for deep networks to extract representative target features, which limits the ship detection performance to a certain extent. In order to tackle the above problems, we propose an improved YOLOv4 (ImYOLOv4) based on attention mechanism. Firstly, to achieve the best trade-off between detection accuracy and speed, we adopt the off-the-shelf YOLOv4 as our basic framework because of its fast detection speed. Secondly, a thresholding attention module (TAM) is introduced to suppress the adverse effect of complex backgrounds and noises. Besides, we embed channel attention module (CAM) into improved BiFPN as the feature pyramid network (FPN) to better enhance the discrimination of the multiscale target features. Finally, the decoupled head with two parallel branches improves the performance of classification and regression. The proposed method is evaluated on public SAR dataset and the experimental results demonstrate that it has higher efficiency and feasibility than other mainstream methods, yielding the accuracy of 94.16% at intersection over union of 0.5 and 58.19% at intersection over union of 0.75.
DOI	10.1109/access.2022.3154474
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收录类别	sci ; ei
语种	英语
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/66752
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Y. L. Gao,Z. Y. Wu,M. Ren and C. A. Wu. Improved YOLOv4 Based on Attention Mechanism for Ship Detection in SAR Images[J]. Ieee Access,2022,10:23785-23797.
APA	Y. L. Gao,Z. Y. Wu,&M. Ren and C. A. Wu.(2022).Improved YOLOv4 Based on Attention Mechanism for Ship Detection in SAR Images.Ieee Access,10,23785-23797.
MLA	Y. L. Gao,et al."Improved YOLOv4 Based on Attention Mechanism for Ship Detection in SAR Images".Ieee Access 10(2022):23785-23797.

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