X-LineNet: Detecting Aircraft in Remote Sensing Images by a Pair of Intersecting Line Segments
Haoran Wei, Yue Zhang, Bing Wang, Yang Yang, Hao Li, Hongqi Wang
Abstract
Motivated by the development of deep convolution neural networks (DCNNs), aircraft detection has gained tremendous progress. State-of-the-art DCNN-based detectors mainly belong to top-down approaches, which enumerate massive potential locations of aircraft with the form of rectangular regions, and then identify whether they are objects or not. Compared with these top-down detectors, this article shows that aircraft detection via a type of bottom-up method can have better performances in the era of deep learning. In this article, we propose a novel bottom-up detector named X-LineNet. It formulates the aircraft detection task as prediction and clustering of paired intersecting line segments inside each target. Aircraft detection is then a purely appearance-based line segments estimation problem, without any rectangular regions classification or implicit features learning. With simple postprocessing, X-LineNet can simultaneously provide multiple representation forms of the detection result: the horizontal bounding box, the oriented bounding box, and the pentagonal mask. The pentagonal mask is a more accurate representation form of aircraft which has less redundancy than that of a rectangular box. Experiments show that X-LineNet outperforms prevalent top-down and region-based detectors on UCAS-AOD, NWPU VHR-10, and DIOR public data sets in the field of aircraft detection.