X-LineNet: Detecting Aircraft in Remote Sensing Images by a pair of Intersecting Line Segments

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.