Frequency-Adaptive Learning for SAR Ship Detection in Clutter Scenes

Zhang, Linping; Liu, Yu; Zhao, Wenda; Wang, Xueqian; Li, Gang; He, You

doi:10.1109/tgrs.2023.3249349

Cited by 21 publications

(11 citation statements)

References 51 publications

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“…The two-stage fusion method employs a two-stage processing pipeline that combines traditional operators with neural networks, demonstrating effectiveness [29]. Through initial preprocessing, leveraging traditional operators such as CFAR, classical operations are performed on SAR images for feature extraction.…”

Section: Fusion-based Methodsmentioning

confidence: 99%

A CFAR-Enhanced Ship Detector for SAR Images Based on YOLOv5s

Wen,

Zhang,

Wang

et al. 2024

Remote Sensing

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Ship detection and recognition in Synthetic Aperture Radar (SAR) images are crucial for maritime surveillance and traffic management. Limited availability of high-quality datasets hinders in-depth exploration of ship features in complex SAR images. While most existing SAR ship research is primarily based on Convolutional Neural Networks (CNNs), and although deep learning advances SAR image interpretation, it often prioritizes recognition over computational efficiency and underutilizes SAR image prior information. Therefore, this paper proposes YOLOv5s-based ship detection in SAR images. Firstly, for comprehensive detection enhancement, we employ the lightweight YOLOv5s model as the baseline. Secondly, we introduce a sub-net into YOLOv5s, learning traditional features to augment ship feature representation of Constant False Alarm Rate (CFAR). Additionally, we attempt to incorporate frequency-domain information into the channel attention mechanism to further improve detection. Extensive experiments on the Ship Recognition and Detection Dataset (SRSDDv1.0) in complex SAR scenarios confirm our method’s 68.04% detection accuracy and 60.25% recall, with a compact 18.51 M model size. Our network surpasses peers in mAP, F1 score, model size, and inference speed, displaying robustness across diverse complex scenes.

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Section: Fusion-based Methodsmentioning

confidence: 99%

A CFAR-Enhanced Ship Detector for SAR Images Based on YOLOv5s

Wen,

Zhang,

Wang

et al. 2024

Remote Sensing

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“…Chang et al [22] simplified the classic YOLOv2 architecture for CNN to reduce calculation time when detection ships in SAR images. Zhang et al [23,24] proposed a variant of YOLO-FA to incorporate the frequency-domain information. Sun et al [25] used fully convolutional one-stage object identification as the foundation of their detector to improve the network's position regression branch features, and bounding box regression detection.…”

Section: Deep Learning-based Sar Ship Detectionmentioning

confidence: 99%

An Effective Multi-Layer Attention Network for SAR Ship Detection

Suo

Zhao

2023

JMSE

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The use of deep learning-based techniques has improved the performance of synthetic aperture radar (SAR) image-based applications, such as ship detection. However, all existing methods have limited object detection performance under the conditions of varying ship sizes and complex background noise, to the best of our knowledge. In this paper, to solve both the multi-scale problem and the noisy background issues, we propose a multi-layer attention approach based on the thorough analysis of both location and semantic information. The solution works by exploring the richness of spatial information of the low-level feature maps generated by a backbone and the richness of semantic information of the high-level feature maps created by the same method. Additionally, we integrate an attention mechanism into the network to exclusively extract useful features from the input maps. Tests involving multiple SAR datasets show that our proposed solution enables significant improvements to the accuracy of ship detection regardless of vessel size and background complexity. Particularly for the widely-adopted High-Resolution SAR Images Dataset (HRSID), the new method provides a 1.3% improvement in the average precision for detection. The proposed new method can be potentially used in other feature-extraction-based classification, detection, and segmentation.

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“…This approach was applied to the input of YOLOv4-tiny to enhance the model’s sensitivity to small targets. Considering the background noise interference characteristic of SAR images, Zhang et al [ 29 ] introduced a Frequency Attention Mechanism (FAM) in YOLOv5s to adaptively process frequency domain information and suppress sea clutter using captured frequency information.…”

Section: Introductionmentioning

confidence: 99%

Lightweight high-precision SAR ship detection method based on YOLOv7-LDS

Zhu,

Miao

2024

PLoS ONE

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The current challenges in Synthetic Aperture Radar (SAR) ship detection tasks revolve around handling significant variations in target sizes and managing high computational expenses, which hinder practical deployment on satellite or mobile airborne platforms. In response to these challenges, this research presents YOLOv7-LDS, a lightweight yet highly accurate SAR ship detection model built upon the YOLOv7 framework. In the core of YOLOv7-LDS’s architecture, we introduce a streamlined feature extraction network that strikes a delicate balance between detection precision and computational efficiency. This network is founded on Shufflenetv2 and incorporates Squeeze-and-Excitation (SE) attention mechanisms as its key elements. Additionally, in the Neck section, we introduce the Weighted Efficient Aggregation Network (DCW-ELAN), a fundamental feature extraction module that leverages Coordinate Attention (CA) and Depthwise Convolution (DWConv). This module efficiently aggregates features while preserving the ability to identify small-scale variations, ensuring top-quality feature extraction. Furthermore, we introduce a lightweight Spatial Pyramid Dilated Convolution Cross-Stage Partial Channel (LSPHDCCSPC) module. LSPHDCCSPC is a condensed version of the Spatial Pyramid Pooling Cross-Stage Partial Channel (SPPCSPC) module, incorporating Dilated Convolution (DConv) as a central component for extracting multi-scale information. The experimental results show that YOLOv7-LDS achieves a remarkable Mean Average Precision (mAP) of 99.1% and 95.8% on the SAR Ship Detection Dataset (SSDD) and the NWPU VHR-10 dataset with a parameter count (Params) of 3.4 million, a Giga Floating Point Operations Per Second (GFLOPs) of 6.1 and an Inference Time (IT) of 4.8 milliseconds. YOLOv7-LDS effectively strikes a fine balance between computational cost and detection performance, surpassing many of the current state-of-the-art object detection models. As a result, it offers a more resilient solution for maritime ship monitoring.

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Frequency-Adaptive Learning for SAR Ship Detection in Clutter Scenes

Cited by 21 publications

References 51 publications

A CFAR-Enhanced Ship Detector for SAR Images Based on YOLOv5s

A CFAR-Enhanced Ship Detector for SAR Images Based on YOLOv5s

An Effective Multi-Layer Attention Network for SAR Ship Detection

Lightweight high-precision SAR ship detection method based on YOLOv7-LDS

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