Much attention is being paid to using high-performance convolutional neural networks (CNNs) in the area of ship detection in optical remoting sensing (ORS) images. However, the problem of false negatives (FNs) caused by side-by-side ships cannot be solved, and the number of false positives (FPs) remains high. This paper uses a DLA-34 network with deformable convolution layers as the backbone. The network has two priority branches: a recall-priority branch for reducing the number of FNs, and a precision-priority branch for reducing the number of FPs. In our single-shot detection method, the recall-priority branch is based on an anchor-free module without non-maximum suppression (NMS), while the precision-priority branch utilizes an anchor-based module with NMS. We perform recall-priority branch functions based on the output part of the CenterNet object detector to precisely predict center points of bounding boxes. The Bidirectional Feature Pyramid Network (BiFPN), combined with the inference part of YOLOv3, is used to improve the precision of precision-priority branch. Finally, the boxes from two branches merge, and we propose priority-based selection (PBS) for choosing the accurate ones. Results show that our proposed method sharply improves the recall rate of side-by-side ships and significantly reduces the number of false alarms. Our method also achieves the best trade-off on our improved version of HRSC2016 dataset, with 95.57% AP at 56 frames per second on an Nvidia RTX-2080 Ti GPU. Compared with the HRSC2016 dataset, not only are our annotations more accurate, but our dataset also contains more images and samples. Our evaluation metrics also included tests on small ships and incomplete forms of ships.

中文翻译：

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光学遥感影像中船舶探测的优先分支

在光学远程感测（ORS）图像中的舰船检测领域，使用高性能卷积神经网络（CNN）引起了人们的极大关注。但是，并排船造成的假阴性（FN）问题无法解决，假阳性（FP）的数量仍然很高。本文使用具有可变形卷积层的DLA-34网络作为骨干网。该网络具有两个优先级分支：用于减少FN数量的召回优先级分支，以及用于减少FP数量的精度优先级分支。在我们的单发检测方法中，召回优先级分支基于不带锚点的模块，而没有非最大抑制（NMS），而精确优先级分支则利用基于锚点的模块和NMS。我们根据CenterNet对象检测器的输出部分执行召回优先分支功能，以精确预测边界框的中心点。双向特征金字塔网络（BiFPN）与YOLOv3的推理部分相结合，用于提高精度优先分支的精度。最后，来自两个分支的框合并，我们提出了基于优先级的选择（PBS）以选择准确的框。结果表明，我们提出的方法大大提高了并排船舶的召回率，并大大减少了误报的次数。我们的方法还在改进版本的HRSC2016数据集上实现了最佳权衡，在Nvidia RTX-2080 Ti GPU上以每秒56帧的速度获得95.57％的AP。与HRSC2016数据集相比，我们的注释不仅更准确，但我们的数据集还包含更多图像和样本。我们的评估指标还包括对小型船舶和不完整形式的船舶的测试。