航空图像中的微小对象检测(TOD)是具有挑战性的,因为一个小物体只包含几个像素。最先进的对象探测器由于缺乏判别特征的监督而无法为微小对象提供令人满意的结果。我们的主要观察结果是,联合度量(IOU)及其扩展的相交对微小物体的位置偏差非常敏感,这在基于锚固的探测器中使用时会大大恶化标签分配的质量。为了解决这个问题,我们提出了一种新的评估度量标准,称为标准化的Wasserstein距离(NWD)和一个新的基于排名的分配(RKA)策略,以进行微小对象检测。提出的NWD-RKA策略可以轻松地嵌入到各种基于锚的探测器中,以取代标准的基于阈值的检测器,从而大大改善了标签分配并为网络培训提供了足够的监督信息。在四个数据集中测试,NWD-RKA可以始终如一地提高微小的对象检测性能。此外,在空中图像(AI-TOD)数据集中观察到显着的嘈杂标签,我们有动力将其重新标记并释放AI-TOD-V2及其相应的基准。在AI-TOD-V2中,丢失的注释和位置错误问题得到了大大减轻,从而促进了更可靠的培训和验证过程。将NWD-RKA嵌入探测器中,检测性能比AI-TOD-V2上的最先进竞争对手提高了4.3个AP点。数据集,代码和更多可视化可在以下网址提供:https://chasel-tsui.g​​ithub.io/ai/ai-tod-v2/
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检测微小的物体是一个非常具有挑战性的问题,因为一个小物体只包含几个像素的大小。我们证明,由于缺乏外观信息,最新的检测器不会对微小物体产生令人满意的结果。我们的主要观察结果是,基于联合(IOU)的相交(例如IOU本身及其扩展)对微小物体的位置偏差非常敏感,并且在基于锚固的检测器中使用时会大大恶化检测性能。为了减轻这一点,我们提出了使用Wasserstein距离进行微小对象检测的新评估度量。具体而言,我们首先将边界框建模为2D高斯分布,然后提出一个新的公制称为标准化的瓦斯汀距离(NWD),以通过相应的高斯分布来计算它们之间的相似性。提出的NWD度量可以轻松地嵌入分配中,非最大抑制作用以及任何基于锚固的检测器的损耗函数,以替换常用的IOU度量。我们在新的数据集上评估了我们的度量,以用于微小对象检测(AI-TOD),其中平均对象大小比现有对象检测数据集小得多。广泛的实验表明,在配备NWD指标时,我们的方法的性能比标准的微调基线高6.7 AP点,并且比最先进的竞争对手高6.0 AP点。代码可在以下网址提供:https://github.com/jwwangchn/nwd。
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检测小物体是阻碍对象检测开发的主要障碍之一。通用对象检测器的性能在微小的对象检测任务上往往会大大恶化。在本文中,我们指出的是,基于锚的检测器中的先验盒或无锚检测器中的点是微小对象的优化。我们的主要观察结果是,当前基于锚的或无锚的标签分配范例将引起许多离群的微小地面真实样本,从而导致检测器对小物体的关注较少。为此,我们提出了一个基于高斯接受场的标签分配(RFLA)策略,以进行微小的对象检测。具体而言,RFLA首先利用了特征接受场遵循高斯分布的先前信息。然后,提出了一个新的接受场距离(RFD),而不是通过IOU或中心采样策略分配样品,以直接测量高斯接受场和地面真相之间的相似性。考虑到基于阈值的和中心的采样策略偏向大物体,我们进一步设计了基于RFD的层次标签分配(HLA)模块,以实现微小对象的平衡学习。四个数据集上的广泛实验证明了所提出的方法的有效性。尤其是,我们的方法在AI-TOD数据集上以4.0 AP点优于最先进的竞争对手。代码可从https://github.com/chasel-tsui/mmdet-rfla获得
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随着深度卷积神经网络的兴起,对象检测在过去几年中取得了突出的进步。但是,这种繁荣无法掩盖小物体检测(SOD)的不令人满意的情况,这是计算机视觉中臭名昭著的挑战性任务之一,这是由于视觉外观不佳和由小目标的内在结构引起的嘈杂表示。此外,用于基准小对象检测方法基准测试的大规模数据集仍然是瓶颈。在本文中,我们首先对小物体检测进行了详尽的审查。然后,为了催化SOD的发展,我们分别构建了两个大规模的小物体检测数据集(SODA),SODA-D和SODA-A,分别集中在驾驶和空中场景上。 SODA-D包括24704个高质量的交通图像和277596个9个类别的实例。对于苏打水,我们收集2510个高分辨率航空图像,并在9个类别上注释800203实例。众所周知,拟议的数据集是有史以来首次尝试使用针对多类SOD量身定制的大量注释实例进行大规模基准测试。最后,我们评估主流方法在苏打水上的性能。我们预计发布的基准可以促进SOD的发展,并产生该领域的更多突破。数据集和代码将很快在:\ url {https://shaunyuan22.github.io/soda}上。
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在过去的十年中,由于航空图像引起的物体的规模和取向的巨大变化,对象检测已经实现了自然图像中的显着进展,而不是在空中图像中。更重要的是,缺乏大规模基准已成为在航拍图像(ODAI)中对物体检测发展的主要障碍。在本文中,我们在航空图像(DotA)中的物体检测和用于ODAI的综合基线的大规模数据集。所提出的DOTA数据集包含1,793,658个对象实例,18个类别的面向边界盒注释从11,268个航拍图像中收集。基于该大规模和注释的数据集,我们构建了具有超过70个配置的10个最先进算法的基线,其中已经评估了每个模型的速度和精度性能。此外,我们为ODAI提供了一个代码库,并建立一个评估不同算法的网站。以前在Dota上运行的挑战吸引了全球1300多队。我们认为,扩大的大型DOTA数据集,广泛的基线,代码库和挑战可以促进鲁棒算法的设计和对空中图像对象检测问题的可再现研究。
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Single-frame InfraRed Small Target (SIRST) detection has been a challenging task due to a lack of inherent characteristics, imprecise bounding box regression, a scarcity of real-world datasets, and sensitive localization evaluation. In this paper, we propose a comprehensive solution to these challenges. First, we find that the existing anchor-free label assignment method is prone to mislabeling small targets as background, leading to their omission by detectors. To overcome this issue, we propose an all-scale pseudo-box-based label assignment scheme that relaxes the constraints on scale and decouples the spatial assignment from the size of the ground-truth target. Second, motivated by the structured prior of feature pyramids, we introduce the one-stage cascade refinement network (OSCAR), which uses the high-level head as soft proposals for the low-level refinement head. This allows OSCAR to process the same target in a cascade coarse-to-fine manner. Finally, we present a new research benchmark for infrared small target detection, consisting of the SIRST-V2 dataset of real-world, high-resolution single-frame targets, the normalized contrast evaluation metric, and the DeepInfrared toolkit for detection. We conduct extensive ablation studies to evaluate the components of OSCAR and compare its performance to state-of-the-art model-driven and data-driven methods on the SIRST-V2 benchmark. Our results demonstrate that a top-down cascade refinement framework can improve the accuracy of infrared small target detection without sacrificing efficiency. The DeepInfrared toolkit, dataset, and trained models are available at https://github.com/YimianDai/open-deepinfrared to advance further research in this field.
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物体检测在计算机视觉中取得了巨大的进步。具有外观降级的小物体检测是一个突出的挑战,特别是对于鸟瞰观察。为了收集足够的阳性/阴性样本进行启发式训练,大多数物体探测器预设区域锚,以便将交叉联盟(iou)计算在地面判处符号数据上。在这种情况下,小物体经常被遗弃或误标定。在本文中,我们提出了一种有效的动态增强锚(DEA)网络,用于构建新颖的训练样本发生器。与其他最先进的技术不同,所提出的网络利用样品鉴别器来实现基于锚的单元和无锚单元之间的交互式样本筛选,以产生符合资格的样本。此外,通过基于保守的基于锚的推理方案的多任务联合训练增强了所提出的模型的性能,同时降低计算复杂性。所提出的方案支持定向和水平对象检测任务。对两个具有挑战性的空中基准(即,DotA和HRSC2016)的广泛实验表明,我们的方法以适度推理速度和用于训练的计算开销的准确性实现最先进的性能。在DotA上,我们的DEA-NET与ROI变压器的基线集成了0.40%平均平均精度(MAP)的先进方法,以便用较弱的骨干网(Resnet-101 VS Resnet-152)和3.08%平均 - 平均精度(MAP),具有相同骨干网的水平对象检测。此外,我们的DEA网与重新排列的基线一体化实现最先进的性能80.37%。在HRSC2016上,它仅使用3个水平锚点超过1.1%的最佳型号。
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The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.
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Object detection has been dominated by anchor-based detectors for several years. Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal Loss. In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them. If they adopt the same definition of positive and negative samples during training, there is no obvious difference in the final performance, no matter regressing from a box or a point. This shows that how to select positive and negative training samples is important for current object detectors. Then, we propose an Adaptive Training Sample Selection (ATSS) to automatically select positive and negative samples according to statistical characteristics of object. It significantly improves the performance of anchor-based and anchor-free detectors and bridges the gap between them. Finally, we discuss the necessity of tiling multiple anchors per location on the image to detect objects. Extensive experiments conducted on MS COCO support our aforementioned analysis and conclusions. With the newly introduced ATSS, we improve stateof-the-art detectors by a large margin to 50.7% AP without introducing any overhead. The code is available at https://github.com/sfzhang15/ATSS.
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检测定向对象以及估计其旋转信息是用于分析遥感图像的一个关键步骤。尽管最近提出了许多方法,但大多数人直接学习在仅单独的一个(例如旋转角度)的监督下预测对象方向或仅为几(例如旋转角度)或几(例如若干坐标)地基值。在训练期间采用了关于提议和旋转信息回归的额外约束,在额外约束,在训练期间采用了更准确的对象检测。为此,我们创新地提出了一种通过Naive几何计算以一致的方式同时学习物体的水平提出,面向建议和旋转角度的机制,作为一个额外的稳定约束(参见图1)。提出了一个导向的中心先前引导标签分配策略,以进一步提高建议的质量,产生更好的性能。广泛的实验表明,配备我们的想法的模型显着优于基线,通过大幅度来实现新的最先进的结果,在推理期间没有任何额外的计算负担。我们提出的想法简单直观,可以随时实现。源代码和培训的型号涉及补充文件。
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We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often very sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), FCOS with ResNeXt-64x4d-101 achieves 44.7% in AP with single-model and single-scale testing, surpassing previous one-stage detectors with the advantage of being much simpler. For the first time, we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. Code is available at:tinyurl.com/FCOSv1
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无锚的检测器基本上将对象检测作为密集的分类和回归。对于流行的无锚检测器,通常是引入单个预测分支来估计本地化的质量。当我们深入研究分类和质量估计的实践时,会观察到以下不一致之处。首先,对于某些分配了完全不同标签的相邻样品,训练有素的模型将产生相似的分类分数。这违反了训练目标并导致绩效退化。其次,发现检测到具有较高信心的边界框与相应的地面真相具有较小的重叠。准确的局部边界框将被非最大抑制(NMS)过程中的精确量抑制。为了解决不一致问题,提出了动态平滑标签分配(DSLA)方法。基于最初在FCO中开发的中心概念,提出了平稳的分配策略。在[0,1]中将标签平滑至连续值,以在正样品和负样品之间稳定过渡。联合(IOU)在训练过程中会动态预测,并与平滑标签结合。分配动态平滑标签以监督分类分支。在这样的监督下,质量估计分支自然合并为分类分支,这简化了无锚探测器的体系结构。全面的实验是在MS Coco基准上进行的。已经证明,DSLA可以通过减轻上述无锚固探测器的不一致来显着提高检测准确性。我们的代码在https://github.com/yonghaohe/dsla上发布。
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现有检测方法通常使用参数化边界框(Bbox)进行建模和检测(水平)对象,并将其他旋转角参数用于旋转对象。我们认为,这种机制在建立有效的旋转检测回归损失方面具有根本的局限性,尤其是对于高精度检测而言,高精度检测(例如0.75)。取而代之的是,我们建议将旋转的对象建模为高斯分布。一个直接的优势是,我们关于两个高斯人之间距离的新回归损失,例如kullback-leibler Divergence(KLD)可以很好地对齐实际检测性能度量标准,这在现有方法中无法很好地解决。此外,两个瓶颈,即边界不连续性和正方形的问题也消失了。我们还提出了一种有效的基于高斯度量的标签分配策略,以进一步提高性能。有趣的是,通过在基于高斯的KLD损失下分析Bbox参数的梯度,我们表明这些参数通过可解释的物理意义进行了动态更新,这有助于解释我们方法的有效性,尤其是对于高精度检测。我们使用量身定制的算法设计将方法从2-D扩展到3-D,以处理标题估计,并在十二个公共数据集(2-D/3-D,空中/文本/脸部图像)上进行了各种基本检测器的实验结果。展示其优越性。
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In object detection, the intersection over union (IoU) threshold is frequently used to define positives/negatives. The threshold used to train a detector defines its quality. While the commonly used threshold of 0.5 leads to noisy (low-quality) detections, detection performance frequently degrades for larger thresholds. This paradox of high-quality detection has two causes: 1) overfitting, due to vanishing positive samples for large thresholds, and 2) inference-time quality mismatch between detector and test hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, composed of a sequence of detectors trained with increasing IoU thresholds, is proposed to address these problems. The detectors are trained sequentially, using the output of a detector as training set for the next. This resampling progressively improves hypotheses quality, guaranteeing a positive training set of equivalent size for all detectors and minimizing overfitting. The same cascade is applied at inference, to eliminate quality mismatches between hypotheses and detectors. An implementation of the Cascade R-CNN without bells or whistles achieves state-of-the-art performance on the COCO dataset, and significantly improves high-quality detection on generic and specific object detection datasets, including VOC, KITTI, CityPerson, and WiderFace. Finally, the Cascade R-CNN is generalized to instance segmentation, with nontrivial improvements over the Mask R-CNN. To facilitate future research, two implementations are made available at https://github.com/zhaoweicai/cascade-rcnn (Caffe) and https://github.com/zhaoweicai/Detectron-Cascade-RCNN (Detectron).
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我们提出对象盒,这是一种新颖的单阶段锚定且高度可推广的对象检测方法。与现有的基于锚固的探测器和无锚的探测器相反,它们更偏向于其标签分配中的特定对象量表,我们仅将对象中心位置用作正样本,并在不同的特征级别中平均处理所有对象,而不论对象'尺寸或形状。具体而言,我们的标签分配策略将对象中心位置视为形状和尺寸不足的锚定,并以无锚固的方式锚定,并允许学习每个对象的所有尺度。为了支持这一点,我们将新的回归目标定义为从中心单元位置的两个角到边界框的四个侧面的距离。此外,为了处理比例变化的对象,我们提出了一个量身定制的损失来处理不同尺寸的盒子。结果,我们提出的对象检测器不需要在数据集中调整任何依赖数据集的超参数。我们在MS-Coco 2017和Pascal VOC 2012数据集上评估了我们的方法,并将我们的结果与最先进的方法进行比较。我们观察到,与先前的作品相比,对象盒的性能优惠。此外,我们执行严格的消融实验来评估我们方法的不同组成部分。我们的代码可在以下网址提供:https://github.com/mohsenzand/objectbox。
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多年来,使用单点监督的对象检测受到了越来越多的关注。在本文中,我们将如此巨大的性能差距归因于产生高质量的提案袋的失败,这对于多个实例学习至关重要(MIL)。为了解决这个问题,我们引入了现成建议方法(OTSP)方法的轻量级替代方案,从而创建点对点网络(P2BNET),该网络可以通过在中生成建议袋来构建一个互平衡的提案袋一种锚点。通过充分研究准确的位置信息,P2BNET进一步构建了一个实例级袋,避免了多个物体的混合物。最后,以级联方式进行的粗到精细政策用于改善提案和地面真相(GT)之间的IOU。从这些策略中受益,P2BNET能够生产出高质量的实例级袋以进行对象检测。相对于MS可可数据集中的先前最佳PSOD方法,P2BNET将平均平均精度(AP)提高了50%以上。它还证明了弥合监督和边界盒监督检测器之间的性能差距的巨大潜力。该代码将在github.com/ucas-vg/p2bnet上发布。
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We propose CornerNet, a new approach to object detection where we detect an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network. By detecting objects as paired keypoints, we eliminate the need for designing a set of anchor boxes commonly used in prior single-stage detectors. In addition to our novel formulation, we introduce corner pooling, a new type of pooling layer that helps the network better localize corners. Experiments show that Corner-Net achieves a 42.2% AP on MS COCO, outperforming all existing one-stage detectors.
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Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no matter whether there exist objects or not. This paradigm, although effective, is inefficient because the detectors have to go through all patches, severely hindering the inference speed. This paper presents an Objectness Activation Network (OAN) to help detectors focus on fewer patches but achieve more efficient inference and more accurate results, enabling a simple and effective solution to object detection in large images. In brief, OAN is a light fully-convolutional network for judging whether each patch contains objects or not, which can be easily integrated into many object detectors and jointly trained with them end-to-end. We extensively evaluate our OAN with five advanced detectors. Using OAN, all five detectors acquire more than 30.0% speed-up on three large-scale aerial image datasets, meanwhile with consistent accuracy improvements. On extremely large Gaofen-2 images (29200$\times$27620 pixels), our OAN improves the detection speed by 70.5%. Moreover, we extend our OAN to driving-scene object detection and 4K video object detection, boosting the detection speed by 112.1% and 75.0%, respectively, without sacrificing the accuracy. Code is available at https://github.com/Ranchosky/OAN.
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尽管近期长尾对象检测成功,但几乎所有长尾对象探测器都是基于两级范式开发的。在实践中,一阶段探测器在行业中更为普遍,因为它们具有简单而快速的管道,易于部署。然而,在长尾情景中,到目前为止,这项工作尚未探讨。在本文中,我们调查了在这种情况下是否可以良好的单级探测器表现良好。我们发现预防一步检测器实现优异性能的主要障碍是:在长尾数据分布下,类别遭受不同程度的正负不平衡问题。传统的焦点损失与所有类别的调制因子相同的调节因子平衡,因此未能处理长尾问题。为了解决这个问题,我们提出了根据其不平衡程度独立地重新平衡不同类别的正面和负样本的损失贡献的均等的联络损失(EFL)。具体而言,EFL采用类别相关调制因子,可以通过不同类别的培训状态来动态调整。对挑战性的LVIS V1基准进行的广泛实验表明了我们提出的方法的有效性。通过端到端培训管道,EF​​L在整体AP方面实现了29.2%,并对稀有类别进行了显着的性能改进,超越了所有现有的最先进的方法。代码可在https://github.com/modeltc/eod上获得。
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大多数最先进的实例级人类解析模型都采用了两阶段的基于锚的探测器,因此无法避免启发式锚盒设计和像素级别缺乏分析。为了解决这两个问题,我们设计了一个实例级人类解析网络,该网络在像素级别上无锚固且可解决。它由两个简单的子网络组成:一个用于边界框预测的无锚检测头和一个用于人体分割的边缘引导解析头。无锚探测器的头继承了像素样的优点,并有效地避免了对象检测应用中证明的超参数的敏感性。通过引入部分感知的边界线索,边缘引导的解析头能够将相邻的人类部分与彼此区分开,最多可在一个人类实例中,甚至重叠的实例。同时,利用了精炼的头部整合盒子级别的分数和部分分析质量,以提高解析结果的质量。在两个多个人类解析数据集(即CIHP和LV-MHP-V2.0)和一个视频实例级人类解析数据集(即VIP)上进行实验,表明我们的方法实现了超过全球级别和实例级别的性能最新的一阶段自上而下的替代方案。
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