旋转- detr:一种基于端到端变换的航空图像定向目标检测器

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Gil-beom Lee, Jinbeom Kim, Taejune Kim, Simon S. Woo
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引用次数: 0

摘要

航空图像中的定向目标检测是一项非常具有挑战性的任务,因为背景和目标的方向任意且通常排列密集。现有的面向目标检测方法采用基于cnn的方法,分为两阶段、一阶段和无锚点三种方法。它们都需要非最大抑制(NMS)来消除重复的预测。近年来,基于变压器的目标检测器通过执行二部匹配直接解决集合预测问题,从而消除了人工设计的组件,达到了一般目标检测中最先进的性能。受此研究启发,我们提出了一种基于变压器的定向目标检测器,命名为旋转DETR,带有定向边界框(OBBs)标记。我们嵌入了评分网络来减少与背景相对应的token。此外,为了向变压器解码器提供具有角度信息的提案,我们应用提案生成器和迭代提案细化模块。在DOTA、UCAS-AOD和DIOR-R数据集上,旋转DETR在单级和无锚定向目标检测器上实现了最先进的性能,只有10%的特征令牌。在实验中,我们证明了评分网络和迭代提议优化模块的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rotated-DETR: an End-to-End Transformer-based Oriented Object Detector for Aerial Images
Oriented object detection in aerial images is a challenging task due to the highly complex backgrounds and objects with arbitrary oriented and usually densely arranged. Existing oriented object detection methods adopt CNN-based methods, and they can be divided into three types: two-stage, one-stage, and anchor-free methods. All of them require non-maximum suppression (NMS) to eliminate the duplicated predictions. Recently, object detectors based on the transformer remove hand-designed components by directly solving set prediction problems via performing bipartite matching, and achieve state-of-the-art performances in general object detection. Motivated by this research, we propose a transformer-based oriented object detector named Rotated DETR with oriented bounding boxes (OBBs) labeling. We embed the scoring network to reduce the tokens corresponding to the background. In addition, we apply a proposal generator and iterative proposal refinement module in order to provide proposals with angle information to the transformer decoder. Rotated DETR achieves state-of-the-art performance on the single-stage and anchor-free oriented object detectors on DOTA, UCAS-AOD, and DIOR-R datasets with only 10% feature tokens. In the experiment, we show the effectiveness of the scoring network and iterative proposal refinement module.
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来源期刊
Applied Computing Review
Applied Computing Review COMPUTER SCIENCE, INFORMATION SYSTEMS-
自引率
40.00%
发文量
8
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