3D计算机断层扫描扫描的肺结核检测在高效的肺癌筛查中起着至关重要的作用。尽管使用CNNS的基于锚的探测器获得的SOTA性能，但是它们需要预定的锚定参数，例如锚点的尺寸，数量和纵横比，并且在处理具有大量尺寸的肺结节时具有有限的鲁棒性。为了克服这些问题，我们提出了一种基于3D球体表示的中心点匹配的检测网络，该检测网络是无锚的，并且自动预测结节的位置，半径和偏移，而无需手动设计结节/锚参数。 SCPM-Net由两种新颖组件组成：球体表示和中心点匹配。首先，为了匹配临床实践中的结节注释，我们用所提出的边界球体替换常用的边界框，以表示具有质心，半径和3D空间局部偏移的结节。引入兼容的基于球体的交叉口损耗功能，以稳定且有效地培训肺结核检测网络。其次，我们通过设计正中心点选择和匹配过程来赋予网络锚定，自然地丢弃预定的锚箱。在线硬示例挖掘和重新聚焦损失随后使CPM过程能够更加强大，导致更准确的点分配和级别不平衡的缓解。此外，为了更好地捕获用于检测的空间信息和3D上下文，我们建议熔化具有特征提取器的多级空间坐标映射，并将它们与3D挤压和激励的关注模块相结合。 Luna16数据集上的实验结果表明，与肺结核检测的现有锚和锚定方法相比，我们所提出的框架达到卓越的性能。

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.

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。

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.

3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications. In this paper, we extend our preliminary work PointRCNN to a novel and strong point-cloud-based 3D object detection framework, the part-aware and aggregation neural network (Part-A 2 net). The whole framework consists of the part-aware stage and the part-aggregation stage. Firstly, the part-aware stage for the first time fully utilizes free-of-charge part supervisions derived from 3D ground-truth boxes to simultaneously predict high quality 3D proposals and accurate intra-object part locations. The predicted intra-object part locations within the same proposal are grouped by our new-designed RoI-aware point cloud pooling module, which results in an effective representation to encode the geometry-specific features of each 3D proposal. Then the part-aggregation stage learns to re-score the box and refine the box location by exploring the spatial relationship of the pooled intra-object part locations. Extensive experiments are conducted to demonstrate the performance improvements from each component of our proposed framework. Our Part-A 2 net outperforms all existing 3D detection methods and achieves new state-of-the-art on KITTI 3D object detection dataset by utilizing only the LiDAR point cloud data. Code is available at https://github.com/sshaoshuai/PointCloudDet3D.

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。

我们提出了一种新颖的形状意识的关系网络，用于内窥镜粘膜颌下粘膜释放（ESD）手术中的准确和实时地标检测。这项任务具有很大的临床意义，但由于复杂的手术环境中出血，照明反射和运动模糊而极其挑战。与现有解决方案相比，通过使用复杂的聚合方案忽略靶向对象之间的几何关系或捕获关系，所提出的网络能够实现令人满意的精度，同时通过充分利用地标之间的空间关系来保持实时性能。我们首先设计一种算法来自动生成关系关键点热量表，其能够直观地代表地标之间的空间关系的先验知识，而无需使用任何额外的手动注释工作。然后，我们开发两个互补正规计划，以逐步将先验知识纳入培训过程。虽然一个方案通过多任务学习引入像素级正则化，但另一个方案通过利用新设计的分组的一致性评估器来实现全局级正则化，该评估将关系约束以越野方式添加到所提出的网络。这两个方案都有利于训练模型，并且可以随时推动才能卸载，以实现实时检测。我们建立了一个大型内部数据集的ESD手术，用于食管癌，以验证我们提出的方法的有效性。广泛的实验结果表明，我们的方法在准确性和效率方面优于最先进的方法，更快地实现了更好的检测结果。在两个下游应用的有希望的结果进一步证实了我们在ESD临床实践中的方法的巨大潜力。

In this study, we propose a lung nodule detection scheme which fully incorporates the clinic workflow of radiologists. Particularly, we exploit Bi-Directional Maximum intensity projection (MIP) images of various thicknesses (i.e., 3, 5 and 10mm) along with a 3D patch of CT scan, consisting of 10 adjacent slices to feed into self-distillation-based Multi-Encoders Network (MEDS-Net). The proposed architecture first condenses 3D patch input to three channels by using a dense block which consists of dense units which effectively examine the nodule presence from 2D axial slices. This condensed information, along with the forward and backward MIP images, is fed to three different encoders to learn the most meaningful representation, which is forwarded into the decoded block at various levels. At the decoder block, we employ a self-distillation mechanism by connecting the distillation block, which contains five lung nodule detectors. It helps to expedite the convergence and improves the learning ability of the proposed architecture. Finally, the proposed scheme reduces the false positives by complementing the main detector with auxiliary detectors. The proposed scheme has been rigorously evaluated on 888 scans of LUNA16 dataset and obtained a CPM score of 93.6\%. The results demonstrate that incorporating of bi-direction MIP images enables MEDS-Net to effectively distinguish nodules from surroundings which help to achieve the sensitivity of 91.5% and 92.8% with false positives rate of 0.25 and 0.5 per scan, respectively.

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.

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。

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。

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.

基于无人机（UAV）基于无人机的视觉对象跟踪已实现了广泛的应用，并且由于其多功能性和有效性而引起了智能运输系统领域的越来越多的关注。作为深度学习革命性趋势的新兴力量，暹罗网络在基于无人机的对象跟踪中闪耀，其准确性，稳健性和速度有希望的平衡。由于开发了嵌入式处理器和深度神经网络的逐步优化，暹罗跟踪器获得了广泛的研究并实现了与无人机的初步组合。但是，由于无人机在板载计算资源和复杂的现实情况下，暹罗网络的空中跟踪仍然在许多方面都面临严重的障碍。为了进一步探索基于无人机的跟踪中暹罗网络的部署，这项工作对前沿暹罗跟踪器进行了全面的审查，以及使用典型的无人机板载处理器进行评估的详尽无人用分析。然后，进行板载测试以验证代表性暹罗跟踪器在现实世界无人机部署中的可行性和功效。此外，为了更好地促进跟踪社区的发展，这项工作分析了现有的暹罗跟踪器的局限性，并进行了以低弹片评估表示的其他实验。最后，深入讨论了基于无人机的智能运输系统的暹罗跟踪的前景。领先的暹罗跟踪器的统一框架，即代码库及其实验评估的结果，请访问https://github.com/vision4robotics/siamesetracking4uav。

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

由于字体，大小，颜色和方向的各种文本变化，任意形状的场景文本检测是一项具有挑战性的任务。大多数现有基于回归的方法求助于回归文本区域的口罩或轮廓点以建模文本实例。但是，回归完整的口罩需要高训练的复杂性，并且轮廓点不足以捕获高度弯曲的文本的细节。为了解决上述限制，我们提出了一个名为TextDCT的新颖的轻巧锚文本检测框架，该框架采用离散的余弦变换（DCT）将文本掩码编码为紧凑型向量。此外，考虑到金字塔层中训练样本不平衡的数量，我们仅采用单层头来进行自上而下的预测。为了建模单层头部的多尺度文本，我们通过将缩水文本区域视为正样本，并通过融合来介绍一个新颖的积极抽样策略，并通过融合来设计特征意识模块（FAM），以实现空间意识和规模的意识丰富的上下文信息并关注更重要的功能。此外，我们提出了一种分割的非量最大抑制（S-NMS）方法，该方法可以过滤低质量的掩模回归。在四个具有挑战性的数据集上进行了广泛的实验，这表明我们的TextDCT在准确性和效率上都获得了竞争性能。具体而言，TextDCT分别以每秒17.2帧（FPS）和F-measure的F-MEASIE达到85.1，而CTW1500和Total-Text数据集的F-Measure 84.9分别为15.1 fps。

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。

Temporal action detection (TAD) is extensively studied in the video understanding community by generally following the object detection pipeline in images. However, complex designs are not uncommon in TAD, such as two-stream feature extraction, multi-stage training, complex temporal modeling, and global context fusion. In this paper, we do not aim to introduce any novel technique for TAD. Instead, we study a simple, straightforward, yet must-known baseline given the current status of complex design and low detection efficiency in TAD. In our simple baseline (termed BasicTAD), we decompose the TAD pipeline into several essential components: data sampling, backbone design, neck construction, and detection head. We extensively investigate the existing techniques in each component for this baseline, and more importantly, perform end-to-end training over the entire pipeline thanks to the simplicity of design. As a result, this simple BasicTAD yields an astounding and real-time RGB-Only baseline very close to the state-of-the-art methods with two-stream inputs. In addition, we further improve the BasicTAD by preserving more temporal and spatial information in network representation (termed as PlusTAD). Empirical results demonstrate that our PlusTAD is very efficient and significantly outperforms the previous methods on the datasets of THUMOS14 and FineAction. Meanwhile, we also perform in-depth visualization and error analysis on our proposed method and try to provide more insights on the TAD problem. Our approach can serve as a strong baseline for future TAD research. The code and model will be released at https://github.com/MCG-NJU/BasicTAD.

由于基于相交的联盟（IOU）优化维持最终IOU预测度量和损失的一致性，因此它已被广泛用于单级2D对象检测器的回归和分类分支。最近，几种3D对象检测方法采用了基于IOU的优化，并用3D iou直接替换了2D iou。但是，由于复杂的实施和效率低下的向后操作，3D中的这种直接计算非常昂贵。此外，基于3D IOU的优化是优化的，因为它对旋转很敏感，因此可能导致训练不稳定性和检测性能恶化。在本文中，我们提出了一种新型的旋转旋转iou（RDIOU）方法，该方法可以减轻旋转敏感性问题，并在训练阶段与3D IOU相比产生更有效的优化目标。具体而言，我们的RDIOU通过将旋转变量解耦为独立术语，但保留3D iou的几何形状来简化回归参数的复杂相互作用。通过将RDIOU纳入回归和分类分支，鼓励网络学习更精确的边界框，并同时克服分类和回归之间的错位问题。基准Kitti和Waymo开放数据集的广泛实验验证我们的RDIOU方法可以为单阶段3D对象检测带来实质性改进。

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/