DOTA: A Large-scale Dataset for Object Detection in Aerial Images

Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of the huge variation in the scale, orientation and shape of the object instances on the earth's surface, but also due to the scarcity of well-annotated datasets of objects in aerial scenes. To advance object detection research in Earth Vision, also known as Earth Observation and Remote Sensing, we introduce a large-scale Dataset for Object deTection in Aerial images (DOTA). To this end, we collect $2806$ aerial images from different sensors and platforms. Each image is of the size about 4000-by-4000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. These DOTA images are then annotated by experts in aerial image interpretation using $15$ common object categories. The fully annotated DOTA images contains $188,282$ instances, each of which is labeled by an arbitrary (8 d.o.f.) quadrilateral To build a baseline for object detection in Earth Vision, we evaluate state-of-the-art object detection algorithms on DOTA. Experiments demonstrate that DOTA well represents real Earth Vision applications and are quite challenging.Comment: Accepted to CVPR 201

Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning

This article presents Holistically-Attracted Wireframe Parsing (HAWP), a method for geometric analysis of 2D images containing wireframes formed by line segments and junctions. HAWP utilizes a parsimonious Holistic Attraction (HAT) field representation that encodes line segments using a closed-form 4D geometric vector field. The proposed HAWP consists of three sequential components empowered by end-to-end and HAT-driven designs: (1) generating a dense set of line segments from HAT fields and endpoint proposals from heatmaps, (2) binding the dense line segments to sparse endpoint proposals to produce initial wireframes, and (3) filtering false positive proposals through a novel endpoint-decoupled line-of-interest aligning (EPD LOIAlign) module that captures the co-occurrence between endpoint proposals and HAT fields for better verification. Thanks to our novel designs, HAWPv2 shows strong performance in fully supervised learning, while HAWPv3 excels in self-supervised learning, achieving superior repeatability scores and efficient training (24 GPU hours on a single GPU). Furthermore, HAWPv3 exhibits a promising potential for wireframe parsing in out-of-distribution images without providing ground truth labels of wireframes.Comment: Journal extension of arXiv:2003.01663; Accepted by IEEE TPAMI; Code is available at https://github.com/cherubicxn/haw

Learning Regional Attraction for Line Segment Detection

This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map, the proposed regional attraction first establishes the relationship between line segments and regions in the image lattice. Based on this, the line segment map is equivalently transformed to an attraction field map (AFM), which can be remapped to a set of line segments without loss of information. Accordingly, we develop an end-to-end framework to learn attraction field maps for raw input images, followed by a squeeze module to detect line segments. Apart from existing works, the proposed detector properly handles the local ambiguity and does not rely on the accurate identification of edge pixels. Comprehensive experiments on the Wireframe dataset and the YorkUrban dataset demonstrate the superiority of our method. In particular, we achieve an F-measure of 0.831 on the Wireframe dataset, advancing the state-of-the-art performance by 10.3 percent.Comment: Accepted to IEEE TPAMI. arXiv admin note: text overlap with arXiv:1812.0212

Holistically-Attracted Wireframe Parsing

This paper presents a fast and parsimonious parsing method to accurately and robustly detect a vectorized wireframe in an input image with a single forward pass. The proposed method is end-to-end trainable, consisting of three components: (i) line segment and junction proposal generation, (ii) line segment and junction matching, and (iii) line segment and junction verification. For computing line segment proposals, a novel exact dual representation is proposed which exploits a parsimonious geometric reparameterization for line segments and forms a holistic 4-dimensional attraction field map for an input image. Junctions can be treated as the "basins" in the attraction field. The proposed method is thus called Holistically-Attracted Wireframe Parser (HAWP). In experiments, the proposed method is tested on two benchmarks, the Wireframe dataset, and the YorkUrban dataset. On both benchmarks, it obtains state-of-the-art performance in terms of accuracy and efficiency. For example, on the Wireframe dataset, compared to the previous state-of-the-art method L-CNN, it improves the challenging mean structural average precision (msAP) by a large margin ($2.8\%$ absolute improvements) and achieves 29.5 FPS on single GPU ($89\%$ relative improvement). A systematic ablation study is performed to further justify the proposed method.Comment: Accepted by CVPR 202

A huge-amplitude white-light superflare on a L0 brown dwarf discovered by GWAC survey

White-light superflares from ultra cool stars are thought to be resulted from magnetic reconnection, but the magnetic dynamics in a fully convective star is not clear yet. In this paper, we report a stellar superflare detected with the Ground Wide Angle Camera (GWAC), along with rapid follow-ups with the F60A, Xinglong 2.16m and LCOGT telescopes. The effective temperature of the counterpart is estimated to be $2200\pm50$K by the BT-Settl model, corresponding to a spectral type of L0. The $R-$band light curve can be modeled as a sum of three exponential decay components, where the impulsive component contributes a fraction of 23\% of the total energy, while the gradual and the shallower decay phases emit 42\% and 35\% of the total energy, respectively. The strong and variable Balmer narrow emission lines indicate the large amplitude flare is resulted from magnetic activity. The bolometric energy released is about $6.4\times10^{33}$ ergs, equivalent to an energy release in a duration of 143.7 hours at its quiescent level. The amplitude of $\Delta R=-8.6$mag ( or $\Delta V=-11.2$ mag), placing it one of the highest amplitudes of any ultra cool star recorded with excellent temporal resolution. We argue that a stellar flare with such rapidly decaying and huge amplitude at distances greater than 1 kpc may be false positive in searching for counterparts of catastrophic events such as gravitational wave events or gamma-ray bursts, which are valuable in time-domain astronomy and should be given more attention.Comment: 9 pages, 5 figures, 1 table, MNRAS accepte