Hierarchical Similarity Alignment for Domain Adaptive Ship Detection in SAR Images

Zhang, Jun; Li, Simin; Dong, Yongfeng; Shi, Zhenwei; Shi, Zhenwei

doi:10.1109/tgrs.2022.3227626

Cited by 11 publications

(4 citation statements)

References 42 publications

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“…Secondly, the scoring scores are adaptively obtained by these learnable parameters. Finally, the scores are passed through the ReLU function to obtain the final adaptive scores, as shown in (3).…”

Section: B Features Extraction Backbone Revcol-c2fmentioning

confidence: 99%

“…T HE development of Synthetic Aperture Radar (SAR) technology has significantly advanced the field of remote sensing target detection, particularly demonstrating outstanding potential in maritime monitoring [1]- [3]. Unlike optical sensors, which are suboptimal in stringent conditions due to sensitivity to weather and lighting conditions, SAR exceeds in various weather conditions and offers higher data rates and larger processing capacities [4].…”

Section: Introductionmentioning

confidence: 99%

“…(Email:txwang1999@163.com; zeng.zhangfan@hubu.edu.cn) Maritime ship detection is a crucial task in the fields of maritime traffic control, maritime resource exploitation, and maritime environmental protection [11]- [13]. Recent studies [1]- [3] have shown that SAR also plays an important role in ship detection due to its unique imaging mechanism. However, SAR data can be subject to various degradation factors, noise effects, and variabilities during the imaging process.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Multiscale Reversible Column Network for SAR Ship Detection

Wang,

Zeng

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Ship detection via Synthetic Aperture Radar(SAR) is widely used in maritime safety and maritime traffic control, et. al. Recently, deep learning methods are employed to improve the performance of SAR ship detection to a large extent. However, the presence of clutter in SAR images and the large scale difference of ships result in the diminished detection performance in complex environments. As such, in this paper, a novel Adaptive Multi-Scale Reversible Column Network is proposed. Firstly, the idea of disentangled feature learning is applied to construct Reversible Column Networks with C2f module to alleviate the problem of large scale difference and the loss of ship information. Secondly, the multiplexed adaptive spatial pyramid pooling module is proposed to alleviate the impact of complex background clutter through multiplexed pooling operations and adaptive fusion. Finally, a novel feature pyramid network with adaptive downsampling module is designed to reduce the information loss caused by downsampling while enhancing the multi-scale ship detection capability. The effectiveness of the proposed method is validated on two public datasets: SSDD and HRSID. The experimental results show that the proposed method is ablet o achieve better results than current state-of-the-art methods for SAR ship detection in complex environments and large scale difference of ships.

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Section: B Features Extraction Backbone Revcol-c2fmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Multiscale Reversible Column Network for SAR Ship Detection

Wang,

Zeng

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…With recent developments in synthetic aperture radar (SAR) technology, SAR images have become an important component in the field of ship detection. Although SAR has been employed in ocean research for over 50 years, efforts are still underway to compre-hend and apply SAR in maritime radar scenes [1]. Several challenges persist in interpreting SAR images; however, its unique technical characteristics endow SAR with an indispen-sable role in the field of maritime monitoring and security [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

MSFA-YOLO: A Multi-Scale SAR Ship Detection Algorithm Based on Fused Attention

Liangjun,

Feng,

Yubin

et al. 2024

IEEE Access

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Leveraging the excellent feature representation capabilities of neural networks, deep learning methods have been widely adopted for object detection in synthetic aperture radar (SAR) images. However, persistent challenges are encountered in SAR ship detection due to factors such as small ship sizes, high noise levels, multiple targets, and scale variations. To address these complexities, in this paper, the MSFA-YOLO algorithm, a novel multiscale SAR ship detection approach em-powered by a fused attention mechanism, is presented. The proposed algorithm incorporates several key enhancements. The fused attention c2fSE module is integrated into the YOLOv8n baseline network to optimize feature extraction for SAR ships. In addition, the DenseASPP module is incorporated to enhance the model's adaptability to ships of varying scales, improving its ca-pability to accommodate larger ships within lower model scales. Furthermore, the Wise-IoU loss function is adopted, and a dynamic non-monotonic focusing mechanism is employed for bounding box loss, significantly enhancing the model's ability to handle low-quality images. Extensive experiments conducted on benchmark datasets, namely SAR-Ship-Dataset, SSDD, and HRSID, validate the robustness and reliability of the proposed model. Experimental results demonstrate significant performance improvements over YOLOv8n: a 3.1% enhancement in mAP75 and a 2.1% boost in mAP50-95 on the SAR-Ship-Dataset, a 0.7% increase in mAP75 and a 0.5% increase in mAP50-95 on the SSDD dataset, and a 1.8% increase in mAP75 and a 0.7% increase in mAP50-95 on the HRSID dataset. Exhibiting superior performance to existing SAR ship detection models in terms of accuracy, the MSFA-YOLO algorithm represents a significant advancement, establishing itself as the current state-of-the-art algorithm in SAR ship detection. INDEX TERMS ship detection; Sar image, YOLO

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Generalized few-shot object detection in remote sensing images

Zhang

Zhu

et al. 2023

ISPRS Journal of Photogrammetry and Remote Sensing

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Hierarchical Similarity Alignment for Domain Adaptive Ship Detection in SAR Images

Cited by 11 publications

References 42 publications

Adaptive Multiscale Reversible Column Network for SAR Ship Detection

Adaptive Multiscale Reversible Column Network for SAR Ship Detection

MSFA-YOLO: A Multi-Scale SAR Ship Detection Algorithm Based on Fused Attention

Generalized few-shot object detection in remote sensing images

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