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 · IEEE Transactions on Geoscience and Remote Sensing · 2020
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.