  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.
