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

Motivated by the development of deep convolution neural networks (DCNNs), tremendous progress has been gained in the field of aircraft detection. These DCNNs based detectors mainly belong to top-down approaches, which first enumerate massive potential locations of objects with the form of rectangular regions, and then identify whether they are objects or not. Compared with these top-down approaches, this paper shows that aircraft detection via bottom-up approach still performs competitively in the era of deep learning. We present a novel one-stage and anchor-free aircraft detection model in a bottom-up manner, which formulates the task as detection of two intersecting line segments inside each target and grouping of them without any rectangular region classification. This model is named as X-LineNet. With simple post-processing, X-LineNet can simultaneously provide multiple representation forms of the detection result: the horizontal bounding box, the rotating bounding box, and the pentagonal mask. The pentagonal mask is a more accurate representation form which has less redundancy and can better represent aircraft than that of rectangular box. Experiments show that X-LineNet outperforms state-of-the-art one-stage object detectors and is competitive compared with advanced two-stage detectors on both UCAS-AOD and NWPU VHR-10 open dataset in the field of aircraft detection.