Efficient Encoder-Decoder Network With Estimated Direction for SAR Ship Detection

Article Remote Sensing

SSS-YOLO: towards more accurate detection for small ships in SAR image

Jingpu Wang et al.

Summary: This paper presents a small-scale ship detection algorithm based on convolutional neural network, focusing on optimizing the detection of small ships in synthetic aperture radar images. The proposed method, evaluated on open datasets, shows superior performance compared to other deep learning models, with a 6.5% improvement in Average Precision and higher detection efficiency than the baseline YOLOv3 model.

REMOTE SENSING LETTERS (2021)

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Article Geochemistry & Geophysics

An Anchor-Free Method Based on Feature Balancing and Refinement Network for Multiscale Ship Detection in SAR Images

Jiamei Fu et al.

Summary: A novel detection method named FBR-Net is proposed in this article, which achieves efficient detection of multiscale SAR ships in complex scenes by eliminating the anchor effect, balancing multiple features, and refining object features.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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Article Computer Science, Artificial Intelligence

A CenterNet plus plus model for ship detection in SAR images

Haoyuan Guo et al.

Summary: This study addresses the challenges of ship detection in SAR images by proposing an effective and stable single-stage detector called CenterNet++, which utilizes modules like feature refinement, feature pyramids fusion, and head enhancement to tackle issues such as small object detection, generating powerful semantic information, and achieving a balance between foreground and background. The proposed method demonstrates state-of-the-art performance on popular SAR image datasets with remarkable accuracy improvement and minimal increase in time cost compared to baseline approaches.

PATTERN RECOGNITION (2021)

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Article Environmental Sciences

Quad-FPN: A Novel Quad Feature Pyramid Network for SAR Ship Detection

Tianwen Zhang et al.

Summary: A novel quad feature pyramid network (Quad-FPN) is proposed for ship detection from synthetic aperture radar (SAR) imagery, with extensive ablation studies conducted to confirm its effectiveness. Experiments on five datasets show Quad-FPN's optimal performance compared to other 12 competitive CNN-based SAR ship detectors. Additionally, satisfactory detection results in actual ship detection further demonstrate Quad-FPN's practical application value in marine surveillance.

REMOTE SENSING (2021)

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Article Environmental Sciences