Sparse R-CNN OBB: Ship Target Detection in SAR Images Based on Oriented Sparse Proposals
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909625297666048 |
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| author | Kamirul, Kamirul Pappas, Odysseas Achim, Alin |
| author_facet | Kamirul, Kamirul Pappas, Odysseas Achim, Alin |
| contents | We present Sparse R-CNN OBB, a novel framework for the detection of oriented objects in SAR images leveraging sparse learnable proposals. The Sparse R-CNN OBB has streamlined architecture and ease of training as it utilizes a sparse set of 300 proposals instead of training a proposals generator on hundreds of thousands of anchors. To the best of our knowledge, Sparse R-CNN OBB is the first to adopt the concept of sparse learnable proposals for the detection of oriented objects, as well as for the detection of ships in Synthetic Aperture Radar (SAR) images. The detection head of the baseline model, Sparse R-CNN, is re-designed to enable the model to capture object orientation. We train the model on RSDD-SAR dataset and provide a performance comparison to state-of-the-art models. Experimental results show that Sparse R-CNN OBB achieves outstanding performance, surpassing most models on both inshore and offshore scenarios. The code is available at: www.github.com/ka-mirul/Sparse-R-CNN-OBB. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07973 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sparse R-CNN OBB: Ship Target Detection in SAR Images Based on Oriented Sparse Proposals Kamirul, Kamirul Pappas, Odysseas Achim, Alin Computer Vision and Pattern Recognition We present Sparse R-CNN OBB, a novel framework for the detection of oriented objects in SAR images leveraging sparse learnable proposals. The Sparse R-CNN OBB has streamlined architecture and ease of training as it utilizes a sparse set of 300 proposals instead of training a proposals generator on hundreds of thousands of anchors. To the best of our knowledge, Sparse R-CNN OBB is the first to adopt the concept of sparse learnable proposals for the detection of oriented objects, as well as for the detection of ships in Synthetic Aperture Radar (SAR) images. The detection head of the baseline model, Sparse R-CNN, is re-designed to enable the model to capture object orientation. We train the model on RSDD-SAR dataset and provide a performance comparison to state-of-the-art models. Experimental results show that Sparse R-CNN OBB achieves outstanding performance, surpassing most models on both inshore and offshore scenarios. The code is available at: www.github.com/ka-mirul/Sparse-R-CNN-OBB. |
| title | Sparse R-CNN OBB: Ship Target Detection in SAR Images Based on Oriented Sparse Proposals |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.07973 |