用300个可学习框实现雷达图像船舶定向检测，精度超主流模型。
Sparse R-CNN OBB: Ship Target Detection in SAR Images Based on Oriented Sparse Proposals
- 仅用300个可学习候选框替代海量锚点，简化结构易训练。
- 在近岸与远海场景下均超越现有主流模型检测性能。
- 首个将稀疏可学习框用于雷达船检与方向识别的工作。
我们提出Sparse R-CNN OBB，一种基于稀疏可学习候选框的SAR图像中定向目标检测新框架。该模型采用300个稀疏候选框，无需训练数十万锚点，架构简洁且易于训练。据我们所知，Sparse R-CNN OBB是首个将稀疏可学习候选框应用于定向目标检测，以及合成孔径雷达（SAR）图像中船舶检测的研究。对基础模型Sparse R-CNN的检测头进行重设计，使其能捕捉物体方向。在RSDD-SAR数据集上训练并对比当前最优模型。实验结果表明，Sparse R-CNN OBB在近岸和远海场景下均表现优异，显著优于多数现有模型。代码已开源：www.github.com/ka-mirul/Sparse-R-CNN-OBB。
原文摘要 · Abstract (English)
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.
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