Sparse R-CNN OBB: Ship Target Detection in SAR Images Based on Oriented Sparse Proposals

Kamirul Kamirul, Odysseas Pappas, Alin Achim
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Abstract

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 also fine-tune the model on RSDD-SAR dataset and provide a performance comparison to state-of-the-art models. Experimental results shows that Sparse R-CNN OBB achieves outstanding performance, surpassing other models on both inshore and offshore scenarios. The code is available at: www.github.com/ka-mirul/Sparse-R-CNN-OBB.
稀疏 R-CNN OBB:基于定向稀疏提议的合成孔径雷达图像中的舰船目标探测
我们提出了稀疏 R-CNN OBB,这是一种利用稀疏可学习提案检测合成孔径雷达图像中定向物体的新型框架。稀疏 R-CNN OBB 架构精简,易于训练,因为它利用了 300 个提案的稀疏集,而不是在数十万个锚点上训练提案生成器。据我们所知,稀疏 R-CNN OBB 是第一个采用稀疏可学习提案概念来检测定向物体以及合成孔径雷达(SAR)图像中的船只的方法。我们对基线模型--稀疏 R-CNN 的检测头进行了重新设计,使该模型能够捕捉物体的方向。我们还在 RSDD-SAR 数据集上对模型进行了微调,并与最先进的模型进行了性能比较。实验结果表明,稀疏 R-CNN OBB 性能突出,在近岸和离岸场景中都超过了其他模型。代码可在以下网址获取:www.github.com/ka-mirul/Sparse-R-CNN-OBB。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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