Toward Better Accuracy-Efficiency Tradeoffs for Oriented SAR Ship Object Detection
In oriented synthetic aperture radar (SAR) ship detection task, convolutional neural network based detectors have dramatically improved the detection performance, but enormous parameters make it difficult to realize model lightweighting. Recently, DETR and its variants have demonstrated excellent pe...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10944503/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850203060384563200 |
|---|---|
| author | Moran Ju Buniu Niu Mulin Li Tengkai Mao Si-nian Jin |
| author_facet | Moran Ju Buniu Niu Mulin Li Tengkai Mao Si-nian Jin |
| author_sort | Moran Ju |
| collection | DOAJ |
| description | In oriented synthetic aperture radar (SAR) ship detection task, convolutional neural network based detectors have dramatically improved the detection performance, but enormous parameters make it difficult to realize model lightweighting. Recently, DETR and its variants have demonstrated excellent performance in object detection task, while model construction through linear layers has great potential in terms of model lightweighting. However, DETR-based models are rarely applied to oriented object detection task, while the network structure relies on manual experience and cannot be designed automatically. In this article, we propose a novel neural architecture search based lightweight detector in polar coordinate system with DETR as search space for oriented SAR ship detection, where oriented bounding boxes are encoded and decoded in polar coordinate system to cope with boundary discontinuity problems, and the weight entanglement strategy is adopted to realize automatic and lightweight design of DETR. Meanwhile, we design an oriented multiscale attention to alleviate the problem of sampling a large amount of background due to offset learning. Furthermore, we introduce a downsampling feedforward network to significantly reduce network floating point operations. Finally, we transplant FPDDet head as auxiliary head to improve encoder potential ship feature learning and decoder cross-attention learning. Experimental results show that our models not only achieve DETR lightweighting and real-time detection, but also improve detection performance. Our base models achieve state-of-the-art performance on both RSSDD and RSDD datasets compared to previous best models, with 1.36% and 2.28% improvement in mAP with 32.67 and 32.14 GFLOPs, respectively. |
| format | Article |
| id | doaj-art-40bbdc09fbf848cea62c1982527b9e30 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-40bbdc09fbf848cea62c1982527b9e302025-08-20T02:11:37ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01189666968110.1109/JSTARS.2025.355533010944503Toward Better Accuracy-Efficiency Tradeoffs for Oriented SAR Ship Object DetectionMoran Ju0https://orcid.org/0000-0002-3158-4956Buniu Niu1https://orcid.org/0009-0004-2472-959XMulin Li2https://orcid.org/0009-0009-6744-294XTengkai Mao3https://orcid.org/0009-0002-7044-0733Si-nian Jin4https://orcid.org/0000-0003-3226-365XCollege of Information Science and Technology, Dalian Maritime University, Dalian, ChinaCollege of Information Science and Technology, Dalian Maritime University, Dalian, ChinaCollege of Information Science and Technology, Dalian Maritime University, Dalian, ChinaCollege of Information Science and Technology, Dalian Maritime University, Dalian, ChinaCollege of Information Science and Technology, Dalian Maritime University, Dalian, ChinaIn oriented synthetic aperture radar (SAR) ship detection task, convolutional neural network based detectors have dramatically improved the detection performance, but enormous parameters make it difficult to realize model lightweighting. Recently, DETR and its variants have demonstrated excellent performance in object detection task, while model construction through linear layers has great potential in terms of model lightweighting. However, DETR-based models are rarely applied to oriented object detection task, while the network structure relies on manual experience and cannot be designed automatically. In this article, we propose a novel neural architecture search based lightweight detector in polar coordinate system with DETR as search space for oriented SAR ship detection, where oriented bounding boxes are encoded and decoded in polar coordinate system to cope with boundary discontinuity problems, and the weight entanglement strategy is adopted to realize automatic and lightweight design of DETR. Meanwhile, we design an oriented multiscale attention to alleviate the problem of sampling a large amount of background due to offset learning. Furthermore, we introduce a downsampling feedforward network to significantly reduce network floating point operations. Finally, we transplant FPDDet head as auxiliary head to improve encoder potential ship feature learning and decoder cross-attention learning. Experimental results show that our models not only achieve DETR lightweighting and real-time detection, but also improve detection performance. Our base models achieve state-of-the-art performance on both RSSDD and RSDD datasets compared to previous best models, with 1.36% and 2.28% improvement in mAP with 32.67 and 32.14 GFLOPs, respectively.https://ieeexplore.ieee.org/document/10944503/DETRneural architecture search (NAS)oriented ship detectionsynthetic aperture radar (SAR) |
| spellingShingle | Moran Ju Buniu Niu Mulin Li Tengkai Mao Si-nian Jin Toward Better Accuracy-Efficiency Tradeoffs for Oriented SAR Ship Object Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing DETR neural architecture search (NAS) oriented ship detection synthetic aperture radar (SAR) |
| title | Toward Better Accuracy-Efficiency Tradeoffs for Oriented SAR Ship Object Detection |
| title_full | Toward Better Accuracy-Efficiency Tradeoffs for Oriented SAR Ship Object Detection |
| title_fullStr | Toward Better Accuracy-Efficiency Tradeoffs for Oriented SAR Ship Object Detection |
| title_full_unstemmed | Toward Better Accuracy-Efficiency Tradeoffs for Oriented SAR Ship Object Detection |
| title_short | Toward Better Accuracy-Efficiency Tradeoffs for Oriented SAR Ship Object Detection |
| title_sort | toward better accuracy efficiency tradeoffs for oriented sar ship object detection |
| topic | DETR neural architecture search (NAS) oriented ship detection synthetic aperture radar (SAR) |
| url | https://ieeexplore.ieee.org/document/10944503/ |
| work_keys_str_mv | AT moranju towardbetteraccuracyefficiencytradeoffsfororientedsarshipobjectdetection AT buniuniu towardbetteraccuracyefficiencytradeoffsfororientedsarshipobjectdetection AT mulinli towardbetteraccuracyefficiencytradeoffsfororientedsarshipobjectdetection AT tengkaimao towardbetteraccuracyefficiencytradeoffsfororientedsarshipobjectdetection AT sinianjin towardbetteraccuracyefficiencytradeoffsfororientedsarshipobjectdetection |