Monitoring and identification of ships in remote sensing images is of great significance for port management, marine traffic, marine security, etc. However, due to small size and complex background, ship detection in remote sensing images is still a challenging task. Currently, deep-learning-based detection models need a lot of data and manual annotation, while training data containing ships in remote sensing images may be in limited quantities. To solve this problem, in this paper, we propose a few-shot multi-class ship detection algorithm with attention feature map and multi-relation detector (AFMR) for remote sensing images. We use the basic framework of You Only Look Once (YOLO), and use the attention feature map module to enhance the features of the target. In addition, the multi-relation head module is also used to optimize the detection head of YOLO. Extensive experiments on publicly available HRSC2016 dataset and self-constructed REMEX-FSSD dataset validate that our method achieves a good detection performance.

中文翻译：

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使用注意力特征图和多关系检测器在遥感图像中进行 Few-Shot 多类船舶检测

遥感图像中船舶的监测和识别对于港口管理、海上交通、海上安全等具有重要意义。但是，由于船舶体积小、背景复杂，遥感图像中的船舶检测仍然是一项具有挑战性的任务。目前，基于深度学习的检测模型需要大量数据和人工标注，而遥感图像中包含船舶的训练数据可能数量有限。为了解决这个问题，在本文中，我们提出了一种针对遥感图像的带有注意特征图和多关系检测器（AFMR）的few-shot多类船舶检测算法。我们使用 You Only Look Once (YOLO) 的基本框架，并使用注意特征图模块来增强目标的特征。此外，多关系头模块也用于优化YOLO的检测头。在公开可用的 HRSC2016 数据集和自构建的 REMEX-FSSD 数据集上进行的大量实验验证了我们的方法实现了良好的检测性能。