Existing ship detection approaches in optical remote sensing images often suffer from bottlenecks in inshore scenarios due to the substantial interference. In addition, the ship targets with different orientation angles and large aspect ratios increase the difficulty to accurately profile and locate them in optical remote sensing images. To address the aforementioned issues, a novel dual separation attention network (DSA-Net) based on the skew complete intersection-over-union (SkewCIoU) loss is proposed in this letter. In our DSA-Net, we construct a contextual location module (CLM) as the spatial attention in the backbone stage and a global channel module (GCM) as the channel attention in the neck stage, respectively. The two separated attention modules enhance the discrimination between ship targets and complex inshore interferences. Moreover, a SkewCIoU loss considering both the angles and aspect ratios of ship targets is introduced to obtain a well-trained neural network with more accurate detection performance of slender ships. Experiments on the dataset of high-resolution ship collection 2016 (HRSC2016) manifest the superiority of the proposed algorithm in comparison to the existing state-of-the-art methods.

中文翻译：

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光学遥感图像中任意方向船舶检测的改进注意力引导网络


由于存在大量干扰，现有的光学遥感图像船舶检测方法在近海场景中经常遇到瓶颈。此外，不同方位角和大长宽比的船舶目标增加了光学遥感图像中精确轮廓和定位的难度。为了解决上述问题，本文提出了一种基于倾斜完全交叉并集（SkewCIoU）损失的新型双分离注意网络（DSA-Net）。在我们的 DSA-Net 中，我们分别构建了一个上下文位置模块（CLM）作为骨干阶段的空间注意力和一个全局通道模块（GCM）作为颈部阶段的通道注意力。两个独立的注意力模块增强了船舶目标和复杂近岸干扰之间的区分。此外，引入了同时考虑船舶目标的角度和纵横比的SkewCIoU损失，以获得训练有素的神经网络，对细长船舶具有更准确的检测性能。在2016年高分辨率船舶采集（HRSC2016）数据集上的实验表明，与现有最先进的方法相比，所提出的算法具有优越性。