For their high maneuverability, unmanned aerial vehicles (UAVs) are widely used in object detection, including the detection of ships. However, object detection in aerial images taken by UAV remains a challenge due to the arbitrary shooting perspectives and small proportion of targets. Existing anchor-based detectors, whose performance could be easily affected by the aspect ratios and scales of anchor boxes, could get into difficulties in handling candidate targets with wide shape variations. We propose an efficient anchor-free detector to replace a set of predefined anchor boxes. Specifically, guided attention module, embedded in the feature pyramid structure, is put forward to help low-level feature maps acquire the guiding information of high-level feature maps in the multi-scale fusion stage. Then an intersection-over-union (IoU) prediction head is added to predict the IoU for each predicted box. The output from IoU prediction and classification branches is then evaluated to dynamically generate soft labels without sacrificing the effiency in an attempt to improve the performance of the proposed detector. The results of extensive experiments demonstrate that the performance of our proposed detector is better than that of several current mainstream detectors.

中文翻译：

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无锚网络，引导注意力，用于航空影像中的船舶检测

由于其高机动性，无人飞行器（UAV）被广泛用于物体检测，包括船舶检测。然而，由于任意的拍摄角度和较小的目标比例，无人机在航空图像中进行目标检测仍然是一个挑战。现有的基于锚的探测器的性能很容易受到锚箱的长宽比和比例的影响，在处理形状变化较大的候选目标时可能会遇到困难。我们提出了一种高效的无锚检测器，以取代一组预定义的锚盒。具体地，提出了嵌入在特征金字塔结构中的引导注意模块，以帮助低级别特征图在多尺度融合阶段获取高级特征图的指导信息。然后，添加一个联合上的交点（IoU）预测头，以预测每个预测框的IoU。然后，对IoU预测和分类分支的输出进行评估，以动态生成软标签，而又不牺牲效率，以尝试改善所提出的检测器的性能。大量实验的结果表明，我们提出的检测器的性能优于几种当前主流检测器。