R-SABMNet: A YOLOv8-Based Model for Oriented SAR Ship Detection with Spatial Adaptive Aggregation
Synthetic Aperture Radar (SAR) is extensively utilized in ship detection due to its robust performance under various weather conditions and its capability to operate effectively both during the day and at night. However, ships in SAR images exhibit various characteristics including complex land scat...
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| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/3/551 |
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| author | Xiaoting Li Wei Duan Xikai Fu Xiaolei Lv |
| author_facet | Xiaoting Li Wei Duan Xikai Fu Xiaolei Lv |
| author_sort | Xiaoting Li |
| collection | DOAJ |
| description | Synthetic Aperture Radar (SAR) is extensively utilized in ship detection due to its robust performance under various weather conditions and its capability to operate effectively both during the day and at night. However, ships in SAR images exhibit various characteristics including complex land scattering interference, variable scales, and dense spatial arrangements. Existing algorithms are insufficient in effectively addressing these challenges. To enhance detection accuracy, this paper proposes the Rotated model with Spatial Aggregation and a Balanced-Shifted Mechanism (R-SABMNet) built upon YOLOv8. First, we introduce the Spatial-Guided Adaptive Feature Aggregation (SG-AFA) module, which enhances sensitivity to ship features while suppressing land scattering interference. Subsequently, we propose the Balanced Shifted Multi-Scale Fusion (BSMF) module, which effectively enhances local detail information and improves adaptability to multi-scale targets. Finally, we introduce the Gaussian Wasserstein Distance Loss (GWD), which effectively addresses localization errors arising from angle and scale inconsistencies in dense scenes. Our R-SABMNet outperforms other deep learning-based methods on the SSDD+ and HRSID datasets. Specifically, our method achieves a detection accuracy of 96.32%, a recall of 93.13%, and an average level of accuracy of 95.28% on the SSDD+ dataset. |
| format | Article |
| id | doaj-art-bbdb2a0fec15401bbcaa492bc6a6048f |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-bbdb2a0fec15401bbcaa492bc6a6048f2025-08-20T02:12:33ZengMDPI AGRemote Sensing2072-42922025-02-0117355110.3390/rs17030551R-SABMNet: A YOLOv8-Based Model for Oriented SAR Ship Detection with Spatial Adaptive AggregationXiaoting Li0Wei Duan1Xikai Fu2Xiaolei Lv3School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, ChinaInstitute of Software, Chinese Academy of Sciences, Beijing 100190, ChinaKey Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, ChinaSchool of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, ChinaSynthetic Aperture Radar (SAR) is extensively utilized in ship detection due to its robust performance under various weather conditions and its capability to operate effectively both during the day and at night. However, ships in SAR images exhibit various characteristics including complex land scattering interference, variable scales, and dense spatial arrangements. Existing algorithms are insufficient in effectively addressing these challenges. To enhance detection accuracy, this paper proposes the Rotated model with Spatial Aggregation and a Balanced-Shifted Mechanism (R-SABMNet) built upon YOLOv8. First, we introduce the Spatial-Guided Adaptive Feature Aggregation (SG-AFA) module, which enhances sensitivity to ship features while suppressing land scattering interference. Subsequently, we propose the Balanced Shifted Multi-Scale Fusion (BSMF) module, which effectively enhances local detail information and improves adaptability to multi-scale targets. Finally, we introduce the Gaussian Wasserstein Distance Loss (GWD), which effectively addresses localization errors arising from angle and scale inconsistencies in dense scenes. Our R-SABMNet outperforms other deep learning-based methods on the SSDD+ and HRSID datasets. Specifically, our method achieves a detection accuracy of 96.32%, a recall of 93.13%, and an average level of accuracy of 95.28% on the SSDD+ dataset.https://www.mdpi.com/2072-4292/17/3/551ship detectionland scattering interferencesynthetic aperture radar (SAR)multi-head self-attentiontwo-dimensional Gaussian distribution |
| spellingShingle | Xiaoting Li Wei Duan Xikai Fu Xiaolei Lv R-SABMNet: A YOLOv8-Based Model for Oriented SAR Ship Detection with Spatial Adaptive Aggregation Remote Sensing ship detection land scattering interference synthetic aperture radar (SAR) multi-head self-attention two-dimensional Gaussian distribution |
| title | R-SABMNet: A YOLOv8-Based Model for Oriented SAR Ship Detection with Spatial Adaptive Aggregation |
| title_full | R-SABMNet: A YOLOv8-Based Model for Oriented SAR Ship Detection with Spatial Adaptive Aggregation |
| title_fullStr | R-SABMNet: A YOLOv8-Based Model for Oriented SAR Ship Detection with Spatial Adaptive Aggregation |
| title_full_unstemmed | R-SABMNet: A YOLOv8-Based Model for Oriented SAR Ship Detection with Spatial Adaptive Aggregation |
| title_short | R-SABMNet: A YOLOv8-Based Model for Oriented SAR Ship Detection with Spatial Adaptive Aggregation |
| title_sort | r sabmnet a yolov8 based model for oriented sar ship detection with spatial adaptive aggregation |
| topic | ship detection land scattering interference synthetic aperture radar (SAR) multi-head self-attention two-dimensional Gaussian distribution |
| url | https://www.mdpi.com/2072-4292/17/3/551 |
| work_keys_str_mv | AT xiaotingli rsabmnetayolov8basedmodelfororientedsarshipdetectionwithspatialadaptiveaggregation AT weiduan rsabmnetayolov8basedmodelfororientedsarshipdetectionwithspatialadaptiveaggregation AT xikaifu rsabmnetayolov8basedmodelfororientedsarshipdetectionwithspatialadaptiveaggregation AT xiaoleilv rsabmnetayolov8basedmodelfororientedsarshipdetectionwithspatialadaptiveaggregation |