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.

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。

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。

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。

定向对象检测是在空中图像中的具有挑战性的任务，因为航空图像中的物体以任意的方向显示并且经常密集包装。主流探测器使用五个参数或八个主角表示描述了旋转对象，这遭受了定向对象定义的表示模糊性。在本文中，我们提出了一种基于平行四边形的面积比的新型表示方法，称为ARP。具体地，ARP回归定向对象的最小边界矩形和三个面积比。三个面积比包括指向物体与最小的外接矩形的面积比和两个平行四边形到最小的矩形。它简化了偏移学习，消除了面向对象的角度周期性或标签点序列的问题。为了进一步弥补近横向物体的混淆问题，采用对象和其最小的外缘矩形的面积比来指导每个物体的水平或定向检测的选择。此外，使用水平边界盒和三个面积比的旋转高效交叉点（R-EIOU）丢失和三个面积比旨在优化用于旋转对象的边界盒回归。遥感数据集的实验结果，包括HRSC2016，DOTA和UCAS-AOD，表明我们的方法达到了卓越的检测性能，而不是许多最先进的方法。

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.

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.

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/

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。

面部检测是为了在图像中搜索面部的所有可能区域，并且如果有任何情况，则定位面部。包括面部识别，面部表情识别，面部跟踪和头部姿势估计的许多应用假设面部的位置和尺寸在图像中是已知的。近几十年来，研究人员从Viola-Jones脸上检测器创造了许多典型和有效的面部探测器到当前的基于CNN的CNN。然而，随着图像和视频的巨大增加，具有面部刻度的变化，外观，表达，遮挡和姿势，传统的面部探测器被挑战来检测野外面孔的各种“脸部。深度学习技术的出现带来了非凡的检测突破，以及计算的价格相当大的价格。本文介绍了代表性的深度学习的方法，并在准确性和效率方面提出了深度和全面的分析。我们进一步比较并讨论了流行的并挑战数据集及其评估指标。进行了几种成功的基于深度学习的面部探测器的全面比较，以使用两个度量来揭示其效率：拖鞋和延迟。本文可以指导为不同应用选择合适的面部探测器，也可以开发更高效和准确的探测器。

现有的锚定面向对象检测方法已经实现了惊人的结果，但这些方法需要一些手动预设盒，这引入了额外的超参数和计算。现有的锚定方法通常具有复杂的架构，并且不易部署。我们的目标是提出一种简单易于部署的空中图像检测算法。在本文中，我们介绍了基于FCOS的单级锚定旋转对象检测器（FCOSR），可以在大多数平台上部署。 FCOSR具有简单的架构，包括卷积图层。我们的工作侧重于培训阶段的标签分配策略。我们使用椭圆中心采样方法来定义面向定向框（obb）的合适采样区域。模糊样本分配策略为重叠对象提供合理的标签。为解决采样问题不足，设计了一种多级采样模块。这些策略将更合适的标签分配给培训样本。我们的算法分别在DOTA1.0，DOTA1.5和HRSC2016数据集上实现79.25,75.41和90.15映射。 FCOSR在单规模评估中展示了其他方法的卓越性能。我们将轻量级FCOSR模型转换为Tensorrt格式，该格式在Dota1.0上以10.68 fps在jetson Xavier NX上实现73.93映射。该代码可用于：https：//github.com/lzh420202/fcosr

We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available.

现有的实例分割方法已经达到了令人印象深刻的表现，但仍遭受了共同的困境：一个实例推断出冗余表示（例如，多个框，网格和锚点），这导致了多个重复的预测。因此，主流方法通常依赖于手工设计的非最大抑制（NMS）后处理步骤来选择最佳预测结果，这会阻碍端到端训练。为了解决此问题，我们建议一个称为Uniinst的无盒和无端机实例分割框架，该框架仅对每个实例产生一个唯一的表示。具体而言，我们设计了一种实例意识到的一对一分配方案，即仅产生一个表示（Oyor），该方案根据预测和地面真相之间的匹配质量，动态地为每个实例动态分配一个独特的表示。然后，一种新颖的预测重新排列策略被优雅地集成到框架中，以解决分类评分和掩盖质量之间的错位，从而使学习的表示形式更具歧视性。借助这些技术，我们的Uniinst，第一个基于FCN的盒子和无NMS实例分段框架，实现竞争性能，例如，使用Resnet-50-FPN和40.2 mask AP使用Resnet-101-FPN，使用Resnet-50-FPN和40.2 mask AP，使用Resnet-101-FPN，对抗AP可可测试-DEV的主流方法。此外，提出的实例感知方法对于遮挡场景是可靠的，在重锁定的ochuman基准上，通过杰出的掩码AP优于公共基线。我们的代码将在出版后提供。

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).

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

基准，如Coco，在物体检测中发挥至关重要的作用。然而，现有的基准在规模变化中不足，他们的协议不足以进行公平比较。在本文中，我们介绍了通用尺度对象检测基准（USB）。 USB通过将Coco与最近提出的Waymo Open DataSet和Manga109-S数据集合并了Coco，USB具有对象尺度和图像域的变化。为了实现公平的比较和包容性研究，我们提出了培训和评估议定书。它们有多个部门用于培训时期和评估图像分辨率，如体育中的重量类，以及跨训练协议的兼容性，如通用串行总线的后向兼容性。具体而言，我们要求参与者报告结果，不仅具有更高的协议（更长的培训），而且还有更低的协议（较短培训）。使用所提出的基准和协议，我们分析了八种方法，发现了现有的Coco-偏偏见方法的缺点。代码可在https://github.com/shinya7y/universenet上获得。

Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But recent deep learning object detectors have avoided pyramid representations, in part because they are compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A topdown architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using FPN in a basic Faster R-CNN system, our method achieves state-of-the-art singlemodel results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 6 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available.

Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy and optimization function, etc. In this paper, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network based learning systems.

We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. Computation speed is critical as detection is a necessary component for safety. Existing approaches are, however, expensive in computation due to high dimensionality of point clouds. We utilize the 3D data more efficiently by representing the scene from the Bird's Eye View (BEV), and propose PIXOR, a proposal-free, single-stage detector that outputs oriented 3D object estimates decoded from pixelwise neural network predictions. The input representation, network architecture, and model optimization are especially designed to balance high accuracy and real-time efficiency. We validate PIXOR on two datasets: the KITTI BEV object detection benchmark, and a large-scale 3D vehicle detection benchmark. In both datasets we show that the proposed detector surpasses other state-of-the-art methods notably in terms of Average Precision (AP), while still runs at > 28 FPS.

由于任意方向，大规模和纵横比变化以及物体的极端密度，航行图像中的旋转对象检测仍然具有挑战性。现有的最新旋转对象检测方法主要依赖于基于角度的检测器。但是，角度回归很容易遭受长期的边界问题。为了解决这个问题，我们提出了一个纯粹的无角框架，用于旋转对象检测，称为Point RCNN，该框架主要由Pointrpn和Pointreg组成。特别是，Pointrpn通过用粗到精细的方式转换学到的代表点来生成准确的旋转ROI（RROI），这是由重置的动机。基于学习的Rrois，Pointreg执行角点完善以进行更准确的检测。此外，空中图像通常在类别中严重不平衡，现有方法几乎忽略了这个问题。在本文中，我们还通过实验验证了重新采样罕见类别的图像将稳定训练并进一步改善检测性能。实验表明，我们的点RCNN在常用的空中数据集上实现了新的最先进的检测性能，包括DOTA-V1.0，DOTA-V1.5和HRSC2016。