大多数物体检测方法通过使用非最大抑制（NMS）及其改进版本，如Soft-NMS获取对象，这是一个很长的历史记录，以删除冗余边界框。我们从三个方面挑战那些基于NMS的方法：1）具有最高置信度值的边界框可能不是具有与地面真理盒最大的重叠的真正积极。 2）冗余盒不仅需要抑制，而且对于那些真正的阳性也需要置信度。 3）不需要置信度值排序候选盒，以便可以实现完整的并行性。在本文中，通过信仰传播（BP）的启发，我们提出了置信沟集团（CP簇）来替换基于NMS的方法，这是完全并行化的，以及精度更好。在CP-Cluster中，我们借用BP的消息传递机制来惩罚冗余框，并以迭代方式同时增强真正的阳性直到收敛。我们通过将其应用于各种主流探测器，例如FasterRCNN，SSD，FCO，YOLOV3，YOLOV5，CENTERENET等实验，验证了CP-Cluster的有效性。在MS COCO上的实验表明，我们的插头和游戏方法没有再培训探测器，都能够稳步与基于NMS的方法相比，将分别从0.2到1.9的透明边距提高所有最先进模型的平均地图。源代码在https://github.com/shenyi0220/cp-cluster中获得

Confluence是对对象检测的边界框后处理中的非墨西哥抑制（NMS）替代的新型非交流（IOU）替代方案。它克服了基于IOU的NMS变体的固有局限性，以通过使用归一化的曼哈顿距离启发的接近度度量来表示边界框聚类的更稳定，一致的预测指标来表示边界框群集。与贪婪和柔软的NMS不同，它不仅依赖分类置信度得分来选择最佳边界框，而是选择与给定群集中最接近其他盒子的框并删除高度汇合的相邻框。在MS Coco和CrowdHuman基准测试中，汇合的平均精度最高2.3-3.8％，而平均召回率则与DEACTO标准和ART NMS NMS变体相比，平均召回率最高为5.3-7.2％。广泛的定性分析和阈值灵敏度分析实验支持了定量结果，这支持了结论，即汇合比NMS变体更健壮。 Confluence代表边界框处理中的范式变化，有可能在边界框回归过程中替换IOU。

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).

Modern CNN-based object detectors rely on bounding box regression and non-maximum suppression to localize objects. While the probabilities for class labels naturally reflect classification confidence, localization confidence is absent. This makes properly localized bounding boxes degenerate during iterative regression or even suppressed during NMS. In the paper we propose IoU-Net learning to predict the IoU between each detected bounding box and the matched ground-truth. The network acquires this confidence of localization, which improves the NMS procedure by preserving accurately localized bounding boxes. Furthermore, an optimization-based bounding box refinement method is proposed, where the predicted IoU is formulated as the objective. Extensive experiments on the MS-COCO dataset show the effectiveness of IoU-Net, as well as its compatibility with and adaptivity to several state-of-the-art object detectors.

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。

Non-maximum suppression is an integral part of the object detection pipeline. First, it sorts all detection boxes on the basis of their scores. The detection box M with the maximum score is selected and all other detection boxes with a significant overlap (using a pre-defined threshold) with M are suppressed. This process is recursively applied on the remaining boxes. As per the design of the algorithm, if an object lies within the predefined overlap threshold, it leads to a miss. To this end, we propose Soft-NMS, an algorithm which decays the detection scores of all other objects as a continuous function of their overlap with M. Hence, no object is eliminated in this process. Soft-NMS obtains consistent improvements for the coco-style mAP metric on standard datasets like PASCAL VOC 2007 (1.7% for both R-FCN and Faster-RCNN) and MS-COCO (1.3% for R-FCN and 1.1% for Faster-RCNN) by just changing the NMS algorithm without any additional hyper-parameters. UsingDeformable-RFCN, Soft-NMS improves state-of-the-art in object detection from 39.8% to 40.9% with a single model. Further, the computational complexity of Soft-NMS is the same as traditional NMS and hence it can be efficiently implemented. Since Soft-NMS does not require any extra training and is simple to implement, it can be easily integrated into any object detection pipeline. Code for Soft-NMS is publicly available on GitHub http://bit.ly/ 2nJLNMu.

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。

In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usually produces noisy detections. However, detection performance tends to degrade with increasing the IoU thresholds. Two main factors are responsible for this: 1) overfitting during training, due to exponentially vanishing positive samples, and 2) inference-time mismatch between the IoUs for which the detector is optimal and those of the input hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, is proposed to address these problems. It consists of a sequence of detectors trained with increasing IoU thresholds, to be sequentially more selective against close false positives. The detectors are trained stage by stage, leveraging the observation that the output of a detector is a good distribution for training the next higher quality detector. The resampling of progressively improved hypotheses guarantees that all detectors have a positive set of examples of equivalent size, reducing the overfitting problem. The same cascade procedure is applied at inference, enabling a closer match between the hypotheses and the detector quality of each stage. A simple implementation of the Cascade R-CNN is shown to surpass all single-model object detectors on the challenging COCO dataset. Experiments also show that the Cascade R-CNN is widely applicable across detector architectures, achieving consistent gains independently of the baseline detector strength. The code will be made available at https://github.com/zhaoweicai/cascade-rcnn.

标签分配在现代对象检测模型中起着重要作用。检测模型可能会通过不同的标签分配策略产生完全不同的性能。对于基于锚的检测模型，锚点及其相应的地面真实边界框之间的IO（与联合的交点）是关键要素，因为正面样品和负样品除以IOU阈值。早期对象探测器仅利用所有训练样本的固定阈值，而最近的检测算法则基于基于IOUS到地面真相框的分布而着重于自适应阈值。在本文中，我们介绍了一种简单的同时有效的方法，可以根据预测的培训状态动态执行标签分配。通过在标签分配中引入预测，选择了更高的地面真相对象的高质量样本作为正样本，这可以减少分类得分和IOU分数之间的差异，并生成更高质量的边界框。我们的方法显示了使用自适应标签分配算法和这些正面样本的下限框损失的检测模型的性能的改进，这表明将更多具有较高质量预测盒的样品选择为阳性。

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.

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

在对象检测中，广泛采用了非最大抑制（NMS）方法以删除检测到的密集盒的水平重复，以生成最终的对象实例。但是，由于密集检测框的质量降低，而不是对上下文信息的明确探索，因此通过简单的交叉联盟（IOU）指标的现有NMS方法往往在多面向和长尺寸的对象检测方面表现不佳。通过重复删除与常规NMS方法区分，我们提出了一个新的图形融合网络，称为GFNET，用于多个方向的对象检测。我们的GFNET是可扩展的和适应性熔断的密集检测框，可检测更准确和整体的多个方向对象实例。具体而言，我们首先采用一种局部意识的聚类算法将密集检测框分组为不同的簇。我们将为属于一个集群的检测框构建一个实例子图。然后，我们通过图形卷积网络（GCN）提出一个基于图的融合网络，以学习推理并融合用于生成最终实例框的检测框。在公共可用多面向文本数据集（包括MSRA-TD500，ICDAR2015，ICDAR2017-MLT）和多方向对象数据集（DOTA）上进行广泛实验。

样本分配在现代对象检测方法中起着重要的作用。但是，大多数现有的方法都依靠手动设计来分配正 /负样本，这些样本并未明确建立样本分配和对象检测性能之间的关系。在这项工作中，我们提出了一种基于高参数搜索的新型动态样本分配方案。我们首先将分配给每个地面真理的正样本的数量定义为超参数，并采用替代优化算法来得出最佳选择。然后，我们设计一个动态的样本分配过程，以动态选择每个训练迭代中的最佳阳性数量。实验表明，所得的HPS-DET在不同对象检测基线的基线上带来了改善的性能。此外，我们分析了在不同数据集之间和不同骨架之间转移的高参数可重复使用性，以进行对象检测，这表现出我们方法的优势和多功能性。

在本文中，我们通过将无线电信息结合到最先进的检测方法中提出了一种无线电辅助人类检测框架，包括基于锚的oneStage检测器和两级检测器。我们从无线电信号中提取无线电定位和标识符信息以帮助人类检测，由于哪种错误阳性和假否定的问题可能会大大缓解。对于两个探测器，我们使用基于无线电定位的置信度评分修订来提高检测性能。对于两级检测方法，我们建议利用无线电定位产生的区域提案，而不是依赖于区域提案网络（RPN）。此外，利用无线电标识符信息，还提出了具有无线电定位约束的非最大抑制方法，以进一步抑制假检测并减少错过的检测。模拟Microsoft Coco DataSet和CALTECH步行数据集的实验表明，借助无线电信息可以改善平均平均精度（地图）和最先进的检测方法的错过率。最后，我们在现实世界的情况下进行实验，以展示我们在实践中的提出方法的可行性。

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。

物体检测通常需要在现代深度学习方法中基于传统或锚盒的滑动窗口分类器。但是，这些方法中的任何一个都需要框中的繁琐配置。在本文中，我们提供了一种新的透视图，其中检测对象被激励为高电平语义特征检测任务。与边缘，角落，斑点和其他特征探测器一样，所提出的探测器扫描到全部图像的特征点，卷积自然适合该特征点。但是，与这些传统的低级功能不同，所提出的探测器用于更高级别的抽象，即我们正在寻找有物体的中心点，而现代深层模型已经能够具有如此高级别的语义抽象。除了Blob检测之外，我们还预测了中心点的尺度，这也是直接的卷积。因此，在本文中，通过卷积简化了行人和面部检测作为直接的中心和规模预测任务。这样，所提出的方法享有一个无盒设置。虽然结构简单，但它对几个具有挑战性的基准呈现竞争准确性，包括行人检测和面部检测。此外，执行交叉数据集评估，证明所提出的方法的卓越泛化能力。可以访问代码和模型（https://github.com/liuwei16/csp和https://github.com/hasanirtiza/pedestron）。

In this paper, we introduce an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully convolutional and can be used by easily embedding it into most off-the-shelf detection methods. Our method, termed PolarMask, formulates the instance segmentation problem as predicting contour of instance through instance center classification and dense distance regression in a polar coordinate. Moreover, we propose two effective approaches to deal with sampling high-quality center examples and optimization for dense distance regression, respectively, which can significantly improve the performance and simplify the training process. Without any bells and whistles, PolarMask achieves 32.9% in mask mAP with single-model and single-scale training/testing on the challenging COCO dataset.For the first time, we show that the complexity of instance segmentation, in terms of both design and computation complexity, can be the same as bounding box object detection and this much simpler and flexible instance segmentation framework can achieve competitive accuracy. We hope that the proposed PolarMask framework can serve as a fundamental and strong baseline for single shot instance segmentation task. Code is available at: github.com/xieenze/PolarMask.

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.

遵循机器视觉系统在线自动化质量控制和检查过程的成功之后，这项工作中为两个不同的特定应用提供了一种对象识别解决方案，即，在医院准备在医院进行消毒的手术工具箱中检测质量控制项目，以及检测血管船体中的缺陷，以防止潜在的结构故障。该解决方案有两个阶段。首先，基于单镜头多伯克斯检测器（SSD）的特征金字塔体系结构用于改善检测性能，并采用基于地面真实的统计分析来选择一系列默认框的参数。其次，利用轻量级神经网络使用回归方法来实现定向检测结果。该方法的第一阶段能够检测两种情况下考虑的小目标。在第二阶段，尽管很简单，但在保持较高的运行效率的同时，检测细长目标是有效的。

通常在具有固定预定义类别的完全注销的培训数据上学习对象探测器。但是，通常需要逐步增加类别。通常，在这种情况下，只有用旧课程注释的原始培训集和一些带有新课程的新培训数据。基于有限的数据集，强烈需要一个可以处理所有类别的统一检测器。我们提出了一个实用计划，以实现这项工作。无冲突的损失旨在避免标签歧义，从而在一次训练中导致可接受的探测器。为了进一步提高性能，我们提出了一个重新培训阶段，其中采用蒙特卡洛辍学术来计算定位置信度，以挖掘更准确的边界框，并提出了一种重叠的加权方法，以更好地利用在重新训练期间更好地利用伪注释。广泛的实验证明了我们方法的有效性。