区域建议任务是生成一组包含对象的候选区域。在此任务中，最重要的是在固定数量的建议中提出尽可能多的地面真相候选者。然而，在典型的图像中，与大量容易的负面负面相比，艰难的负面例子太少了，因此地区提案网络很难训练硬质否定。由于这个问题，网络倾向于提出艰苦的负面因素作为候选人，而未能提出地面真相的候选者，这导致性能差。在本文中，我们提出了一个负面的区域建议网络（NRPN），以改善区域建议网络（RPN）。NRPN从RPN的误报中学习，并为RPN提供了严重的负面示例。我们提出的NRPN导致假阳性和更好的RPN性能的降低。经过NRPN培训的RPN可以在Pascal VOC 2007数据集上提高性能。

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features-using the recently popular terminology of neural networks with "attention" mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3], our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.

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.

复杂的水下环境为物体检测带来了新的挑战，例如未平衡的光条件，低对比度，阻塞和水生生物的模仿。在这种情况下，水下相机捕获的物体将变得模糊，并且通用探测器通常会在这些模糊的物体上失败。这项工作旨在从两个角度解决问题：不确定性建模和艰难的例子采矿。我们提出了一个名为Boosting R-CNN的两阶段水下检测器，该检测器包括三个关键组件。首先，提出了一个名为RetinArpn的新区域建议网络，该网络提供了高质量的建议，并考虑了对象和IOU预测，以确定对象事先概率的不确定性。其次，引入了概率推理管道，以结合第一阶段的先验不确定性和第二阶段分类评分，以模拟最终检测分数。最后，我们提出了一种名为Boosting Reweighting的新的硬示例挖掘方法。具体而言，当区域提案网络误认为样品的对象的事先概率时，提高重新加权将在训练过程中增加R-CNN头部样品的分类损失，同时减少具有准确估计的先验的简易样品丢失。因此，可以在第二阶段获得强大的检测头。在推理阶段，R-CNN具有纠正第一阶段的误差以提高性能的能力。在两个水下数据集和两个通用对象检测数据集上进行的全面实验证明了我们方法的有效性和鲁棒性。

We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. SSD is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stages and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, COCO, and ILSVRC datasets confirm that SSD has competitive accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. For 300 × 300 input, SSD achieves 74.3% mAP 1 on VOC2007 test at 59 FPS on a Nvidia Titan X and for 512 × 512 input, SSD achieves 76.9% mAP, outperforming a comparable state-of-the-art Faster R-CNN model. Compared to other single stage methods, SSD has much better accuracy even with a smaller input image size. Code is available at: https://github.com/weiliu89/caffe/tree/ssd .

半弱监督和监督的学习最近在对象检测文献中引起了很大的关注，因为它们可以减轻成功训练深度学习模型所需的注释成本。半监督学习的最先进方法依赖于使用多阶段过程训练的学生老师模型，并大量数据增强。为弱监督的设置开发了自定义网络，因此很难适应不同的检测器。在本文中，引入了一种弱半监督的训练方法，以减少这些训练挑战，但通过仅利用一小部分全标记的图像，并在弱标记图像中提供信息来实现最先进的性能。特别是，我们基于通用抽样的学习策略以在线方式产生伪基真实（GT）边界框注释，消除了对多阶段培训的需求和学生教师网络配置。这些伪GT框是根据通过得分传播过程累积的对象建议的分类得分从弱标记的图像中采样的。 PASCAL VOC数据集的经验结果表明，使用VOC 2007作为完全标记的拟议方法可提高性能5.0％，而VOC 2012作为弱标记数据。同样，有了5-10％的完全注释的图像，我们观察到MAP中的10％以上的改善，表明对图像级注释的适度投资可以大大改善检测性能。

We focus on the task of amodal 3D object detection in RGB-D images, which aims to produce a 3D bounding box of an object in metric form at its full extent. We introduce Deep Sliding Shapes, a 3D ConvNet formulation that takes a 3D volumetric scene from a RGB-D image as input and outputs 3D object bounding boxes. In our approach, we propose the first 3D Region Proposal Network (RPN) to learn objectness from geometric shapes and the first joint Object Recognition Network (ORN) to extract geometric features in 3D and color features in 2D. In particular, we handle objects of various sizes by training an amodal RPN at two different scales and an ORN to regress 3D bounding boxes. Experiments show that our algorithm outperforms the state-of-the-art by 13.8 in mAP and is 200× faster than the original Sliding Shapes. Source code and pre-trained models are available.

对象检测是典型的多任务学习应用程序，其同时优化分类和回归。但是，分类损失总是以基于锚的方法的多任务损失主导，妨碍了任务的一致和平衡优化。在本文中，我们发现转移边界盒可以在分类中改变正面和负样本的划分，意思是分类取决于回归。此外，考虑到不同的数据集，优化器和回归损耗功能，我们总结了关于微调损耗重量的三个重要结论。基于上述结论，我们提出了自适应损失重量调整（ALWA）以根据损失的统计特征来解决优化基于锚的方法的不平衡。通过将Alwa纳入以前的最先进的探测器，我们在Pascal VOC和MS Coco上实现了显着的性能增益，即使是L1，Smoothl1和Ciou丢失。代码可在https://github.com/ywx-hub/alwa获得。

The field of object detection has made significant advances riding on the wave of region-based ConvNets, but their training procedure still includes many heuristics and hyperparameters that are costly to tune. We present a simple yet surprisingly effective online hard example mining (OHEM) algorithm for training region-based ConvNet detectors. Our motivation is the same as it has always beendetection datasets contain an overwhelming number of easy examples and a small number of hard examples. Automatic selection of these hard examples can make training more effective and efficient. OHEM is a simple and intuitive algorithm that eliminates several heuristics and hyperparameters in common use. But more importantly, it yields consistent and significant boosts in detection performance on benchmarks like PASCAL VOC 2007 and 2012. Its effectiveness increases as datasets become larger and more difficult, as demonstrated by the results on the MS COCO dataset. Moreover, combined with complementary advances in the field, OHEM leads to state-of-the-art results of 78.9% and 76.3% mAP on PASCAL VOC 2007 and 2012 respectively.

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance.Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.

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。

在对象检测中，当检测器未能检测到目标对象时，会出现假阴性。为了了解为什么对象检测产生假阴性，我们确定了五个“假负机制”，其中每个机制都描述了检测器体系结构内部的特定组件如何失败。着眼于两阶段和一阶段锚点对象检测器体系结构，我们引入了一个框架，用于量化这些虚假的负面机制。使用此框架，我们调查了为什么更快的R-CNN和视网膜无法检测基准视觉数据集和机器人数据集中的对象。我们表明，检测器的假负机制在计算机视觉基准数据集和机器人部署方案之间存在显着差异。这对为机器人应用程序开发的对象检测器的翻译具有影响。

许多开放世界应用程序需要检测新的对象，但最先进的对象检测和实例分段网络在此任务中不屈服。关键问题在于他们假设没有任何注释的地区应被抑制为否定，这教导了将未经讨犯的对象视为背景的模型。为了解决这个问题，我们提出了一个简单但令人惊讶的强大的数据增强和培训方案，我们呼唤学习来检测每件事（LDET）。为避免抑制隐藏的对象，背景对象可见但未标记，我们粘贴在从原始图像的小区域采样的背景图像上粘贴带有的注释对象。由于仅对这种综合增强的图像培训遭受域名，我们将培训与培训分为两部分：1）培训区域分类和回归头在增强图像上，2）在原始图像上训练掩模头。通过这种方式，模型不学习将隐藏对象作为背景分类，同时概括到真实图像。 LDET导致开放式世界实例分割任务中的许多数据集的重大改进，表现出CoCo上的交叉类别概括的基线，以及对UVO和城市的交叉数据集评估。

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).

弱监督的对象检测（WSOD）是一项任务，可使用仅在图像级注释上训练的模型来检测图像中的对象。当前的最新模型受益于自我监督的实例级别的监督，但是由于弱监督不包括计数或位置信息，因此最常见的``Argmax''标签方法通常忽略了许多对象实例。为了减轻此问题，我们提出了一种新颖的多个实例标记方法，称为对象发现。我们进一步在弱监督下引入了新的对比损失，在该监督下，没有实例级信息可用于采样，称为弱监督对比损失（WSCL）。WSCL旨在通过利用一致的功能来嵌入同一类中的向量来构建对象发现的可靠相似性阈值。结果，我们在2014年和2017年MS-Coco以及Pascal VOC 2012上取得了新的最新结果，并在Pascal VOC 2007上取得了竞争成果。

现代领先的物体探测器是从深层CNN的骨干分类器网络重新批准的两阶段或一级网络。YOLOV3是一种这样的非常熟知的最新状态单次检测器，其采用输入图像并将其划分为相等大小的网格矩阵。具有物体中心的网格单元是负责检测特定对象的电池。本文介绍了一种新的数学方法，为准确紧密绑定函数预测分配每个对象的多个网格。我们还提出了一个有效的离线拷贝粘贴数据增强，用于对象检测。我们提出的方法显着优于一些现有的对象探测器，具有进一步更好的性能的前景。

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。

自动检测武器对于改善个人的安全性和福祉是重要的，仍然是由于各种尺寸，武器形状和外观，这是一项艰巨的任务。查看点变化和遮挡也是使这项任务更加困难的原因。此外，目前的物体检测算法处理矩形区域，但是一个细长和长的步枪可以真正地覆盖区域的一部分区域，其余部分可能包含未经紧的细节。为了克服这些问题，我们提出了一种用于定向意识武器检测的CNN架构，其提供具有改进的武器检测性能的面向边界框。所提出的模型不仅通过将角度作为分类问题的角度分成8个类而且提供方向，而是作为回归问题。对于培训我们的武器检测模型，包括总6400件武器图像的新数据集从网上收集，然后用面向定向的边界框手动注释。我们的数据集不仅提供导向的边界框作为地面真相，还提供了水平边界框。我们还以多种现代对象探测器提供我们的数据集，用于在该领域进一步研究。所提出的模型在该数据集上进行评估，并且与搁板对象检测器的比较分析产生了卓越的拟议模型的性能，以标准评估策略测量。数据集和模型实现在此链接上公开可用：https://bit.ly/2tyzicf。

研究表明，当训练数据缺少注释时，对象检测器的性能下降，即稀疏注释数据。当代方法专注于缺少地面实话注释的代理，无论是伪标签的形式还是通过在训练期间重新称重梯度。在这项工作中，我们重新审视了稀疏注释物体检测的制定。我们观察到稀疏注释的物体检测可以被认为是区域级的半监督对象检测问题。在此洞察力上，我们提出了一种基于区域的半监督算法，它自动识别包含未标记的前景对象的区域。我们的算法然后以不同的方式处理标记和未标记的前景区域，在半监督方法中进行常见做法。为了评估所提出的方法的有效性，我们对普斯卡尔库尔和可可数据集的稀疏注释方法常用的五种分裂进行详尽的实验，并实现最先进的性能。除此之外，我们还表明，我们的方法在标准半监督设置上实现了竞争性能，证明了我们的方法的实力和广泛适用性。

We aim to detect all instances of a category in an image and, for each instance, mark the pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS). Unlike classical bounding box detection, SDS requires a segmentation and not just a box. Unlike classical semantic segmentation, we require individual object instances. We build on recent work that uses convolutional neural networks to classify category-independent region proposals (R-CNN [16]), introducing a novel architecture tailored for SDS. We then use category-specific, topdown figure-ground predictions to refine our bottom-up proposals. We show a 7 point boost (16% relative) over our baselines on SDS, a 5 point boost (10% relative) over state-of-the-art on semantic segmentation, and state-of-the-art performance in object detection. Finally, we provide diagnostic tools that unpack performance and provide directions for future work.