In the field of aircraft detection, tremendous progress has been gained movitated by the development of deep convolution neural networks(DCNNs). At present, most state-of-art models based on DCNNs belong to top-down approaches which take a wide use of anchor mechanism. The obtaining of high accuracy in them relys on the enumeration of massive potentional locations of objects with the form of rectangular bounding box, which is wasteful and less elaborate. In this paper, we present a novel aircraft detection model in a bottom-up manner, which formulated the task as detection of two intersecting line segments inside each target and grouping of them, thus we name it as X-LineNet. As the result of learning more delicate visual grammars information of aircraft, detection results with more concrete details and higher accuracy can be gained by X-LineNet. Just for these advantages, we designed a novel form of detetction results--pentagonal mask which has less redundancy and can better represent airplanes than that of rectangular box in remote sensing images.

本文提出了一种新的基于底层建筑方法的一阶段和无锚点飞机检测模型X-LineNet，该模型能够同时提供多种检测结果的表示形式：水平边界框、旋转边界框和五边形蒙版，实验表明，在飞机检测领域中，X-LineNet在两个公开数据集上的表现优于最先进的单阶段物体检测器，并与先进的双阶段检测器具有竞争力。

X-LineNet: 通过一对相交的线段在遥感图像中检测飞机