Aircraft, as one of the indispensable transport tools, plays an important role in military activities. Therefore, it is a significant task to locate the aircrafts in the remote sensing images. However, the current object detection methods cause a series of problems when applied to the aircraft detection for the remote sensing image, for instance, the problems of low rate of detection accuracy and high rate of missed detection. To address the problems of low rate of detection accuracy and high rate of missed detection, an object detection method for remote sensing image based on bidirectional and dense feature fusion is proposed to detect aircraft targets in sophisticated environments. On the fundamental of the YOLOv3 detection framework, this method adds a feature fusion module to enrich the details of the feature map by mixing the shallow features with the deep features together. Experimental results on the RSOD-DataSet and NWPU-DataSet indicate that the new method raised in the article is capable of improving the problems of low rate of detection accuracy and high rate of missed detection. Meanwhile, the AP for the aircraft increases by 1.57% compared with YOLOv3.

中文翻译：

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基于双向密集特征融合的遥感图像飞行器检测

飞机作为不可缺少的运输工具之一，在军事活动中发挥着重要作用。因此，在遥感影像中定位飞行器是一项重要的任务。然而，目前的目标检测方法在应用于遥感图像的飞行器检测时存在一系列问题，例如检测准确率低、漏检率高的问题。针对检测准确率低、漏检率高的问题，提出一种基于双向密集特征融合的遥感图像目标检测方法，用于复杂环境下的飞行器目标检测。在YOLOv3检测框架的基础上，该方法添加了一个特征融合模块，通过将浅层特征与深层特征混合在一起来丰富特征图的细节。在RSOD-DataSet和NWPU-DataSet上的实验结果表明，本文提出的新方法能够改善检测准确率低和漏检率高的问题。同时，飞机的AP比YOLOv3增加了1.57%。