Despite the great progress of deep learning and target detection in recent years, the accurate detection of the occluded targets in remote sensing images still remains a challenge. In this letter, we propose a new detection method called local attention networks to improve the detection of occluded airplanes. Following the idea of “divide and conquer,” the proposed method is designed by first dividing an airplane target into four visual parts: head, left/right wings, body, and tail, and then considering the detection as the prediction of the individual key points in each of the visual parts. We further introduce an additional attention branch in the standard detection pipeline to enhance the features and make the model focus on individual parts of a target even if it is only partially visible in the image. Detection results and ablation studies on three remote sensing target detection data sets (including two publicly available ones) demonstrate the effectiveness of our method, especially for occluded airplane targets. In addition, our method outperforms the other state-of-the-art detection methods on these data sets.

中文翻译：

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用于遥感图像中被遮挡飞机检测的局部注意网络

尽管近年来深度学习和目标检测取得了很大进展，但准确检测遥感图像中被遮挡的目标仍然是一个挑战。在这封信中，我们提出了一种称为局部注意网络的新检测方法，以改进对被遮挡飞机的检测。遵循“分而治之”的思想，该方法的设计首先将飞机目标分为四个视觉部分：头部、左/右翼、身体和尾部，然后将检测视为对单个关键点的预测点在每个视觉部分。我们进一步在标准检测管道中引入了一个额外的注意力分支，以增强特征并使模型专注于目标的各个部分，即使它在图像中仅部分可见。三个遥感目标检测数据集（包括两个公开可用的数据集）的检测结果和消融研究证明了我们方法的有效性，特别是对于被遮挡的飞机目标。此外，我们的方法在这些数据集上优于其他最先进的检测方法。