基于深度学习的SAR图像海洋涡旋自动检测模型

doi:10.11978/2024242

Abstract

Abstract:

Traditional eddy detection methods based on SAR data require manual threshold setting and feature parameter tuning, making processes complex and difficult to automate. Existing deep learning models also suffer from high rates of false negatives and false positives, failing to meet the accuracy and efficiency requirements of eddy detection. To address these issues, this paper proposes an improved model based on YOLOv8 (you only look once version 8), named EddyDetNet, to overcome the above limitations and enhance both detection accuracy and efficiency. This model introduces an adaptive feature compression module (AFCM) and a multi-scale feature spatial pyramid module (MFSP) in Backbone and Neck, optimizes the Neck structure, and adds a small target detection head in the Head part, thereby improving the detection accuracy of vortices of different scales. Experimental results show that EddyDetNet outperforms YOLOv8 by 2.4%, 3.2%, and 5.5% in precision (p), recall (r), and mean Average Precision (mAP), respectively, while reducing parameter size and computational complexity by 38.1% and 15.8%. Compared to YOLOv8, EddyDetNet reduces computational complexity and parameter size while maintaining high detection accuracy, making it suitable for eddy detection tasks in multi-target and complex background scenarios.

Key words: oceanic eddies, synthetic aperture radar, deep learning, YOLO, object detection

CLC Number:

P731.27

LIU Tailong, XIE Tao, LI Jian, WANG Chao, ZHANG Xuehong. Deep learning-based automatic detection model for ocean eddies in SAR images[J].Journal of Tropical Oceanography, 2025, 44(5): 65-76.

Figures/Tables 9

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References 29

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改进方法	AFCM	MFSP	Neck+Head	p/%	r/%	mAP@0.5/%	参数量/M	GFLOPs
1				88.2	88.6	89.5	24.64	78.7
2	√			86.2	92.5	94.7	15.85	50.2
3		√		88.0	89.2	92.5	24.84	80.2
4			√	88.9	91.0	93.4	24.16	99.6
5	√	√	√	90.6	91.8	95.0	15.26	66.3

模型名称	AP/%(AE)	AP/%(CE)	p/%	r/%	mAP@0.5/%
SSD	47.01	81.24	58.48	56.90	64.12
Faster R-CNN	32.99	79.01	43.67	73.76	56.00
YOLOv7	42.60	66.30	51.90	61.10	54.50
YOLOv8	85.20	93.70	88.20	88.60	89.50
YOLOv9	39.70	78.80	52.30	76.00	59.20
EddyDetNet	94.20	95.80	90.60	91.80	95.00

Deep learning-based automatic detection model for ocean eddies in SAR images

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