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.

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.

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.

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.

The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting information flow in proposal-based instance segmentation framework. Specifically, we enhance the entire feature hierarchy with accurate localization signals in lower layers by bottom-up path augmentation, which shortens the information path between lower layers and topmost feature. We present adaptive feature pooling, which links feature grid and all feature levels to make useful information in each feature level propagate directly to following proposal subnetworks. A complementary branch capturing different views for each proposal is created to further improve mask prediction.These improvements are simple to implement, with subtle extra computational overhead. Our PANet reaches the 1 st place in the COCO 2017 Challenge Instance Segmentation task and the 2 nd place in Object Detection task without large-batch training. It is also state-of-the-art on MVD and Cityscapes. Code is available at https://github. com/ShuLiu1993/PANet.

由于卷积在提取物体的局部上下文中，在过去十年中，对象检测在过去十年中取得了重大进展。但是，对象的尺度是多样的，当前卷积只能处理单尺度输入。因此，传统卷积具有固定接收场在处理这种规模差异问题方面的能力受到限制。多尺度功能表示已被证明是缓解规模差异问题的有效方法。最近的研究主要与某些量表或各个尺度的总体特征采用部分联系，并专注于整个量表的全球信息。但是，跨空间和深度维度的信息被忽略了。受此启发，我们提出了多尺度卷积（MSCONV）来解决此问题。同时考虑到量表，空间和深度信息，MSCONV能够更全面地处理多尺度输入。 MSCONV是有效的，并且在计算上是有效的，只有少量计算成本增加。对于大多数单阶段对象探测器，在检测头中用MSCONV代替传统的卷积可以带来AP的2.5 \％改进（在Coco 2017数据集上），只有3 \％的拖鞋增加了。 MSCONV对于两阶段对象探测器也具有灵活性和有效性。当扩展到主流两阶段对象检测器时，MSCONV的AP可以提高3.0 \％。我们在单尺度测试下的最佳模型在Coco 2017上实现了48.9 \％AP，\ textit {test-dev} Split，它超过了许多最新方法。

We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such as Fast/Faster R-CNN [6,18] that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image. To achieve this goal, we propose position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection. Our method can thus naturally adopt fully convolutional image classifier backbones, such as the latest Residual Networks (ResNets) [9], for object detection. We show competitive results on the PASCAL VOC datasets (e.g., 83.6% mAP on the 2007 set) with the 101-layer ResNet. Meanwhile, our result is achieved at a test-time speed of 170ms per image, 2.5-20× faster than the Faster R-CNN counterpart. Code is made publicly available at: https://github.com/daijifeng001/r-fcn. * This work was done when Yi Li was an intern at Microsoft Research. 2 Only the last layer is fully-connected, which is removed and replaced when fine-tuning for object detection.

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

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。

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 .

This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy. Fast R-CNN trains the very deep VGG16 network 9× faster than R-CNN, is 213× faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3× faster, tests 10× faster, and is more accurate. Fast R-CNN is implemented in Python and C++ (using Caffe) and is available under the open-source MIT License at https: //github.com/rbgirshick/fast-rcnn.

两阶段和基于查询的实例分段方法取得了显着的结果。然而，他们的分段面具仍然非常粗糙。在本文中，我们呈现了用于高质量高效的实例分割的掩模转发器。我们的掩模转发器代替常规密集的张量，而不是在常规密集的张量上进行分解，并表示作为Quadtree的图像区域。我们基于变换器的方法仅处理检测到的错误易于树节点，并并行自我纠正其错误。虽然这些稀疏的像素仅构成总数的小比例，但它们对最终掩模质量至关重要。这允许掩模转换器以低计算成本预测高精度的实例掩模。广泛的实验表明，掩模转发器在三个流行的基准上优于当前实例分段方法，显着改善了COCO和BDD100K上的大型+3.0掩模AP的+3.0掩模AP的大余量和CityScapes上的+6.6边界AP。我们的代码和培训的型号将在http://vis.xyz/pub/transfiner提供。

本文提出了平行残留的双融合特征金字塔网络（PRB-FPN），以快速准确地单光对象检测。特征金字塔（FP）在最近的视觉检测中被广泛使用，但是由于汇总转换，FP的自上而下的途径无法保留准确的定位。随着使用更多层的更深骨干，FP的优势被削弱了。此外，它不能同时准确地检测到小物体。为了解决这些问题，我们提出了一种新的并行FP结构，具有双向（自上而下和自下而上）的融合以及相关的改进，以保留高质量的特征以进行准确定位。我们提供以下设计改进：（1）具有自下而上的融合模块（BFM）的平行分歧FP结构，以高精度立即检测小物体和大对象。 （2）串联和重组（CORE）模块为特征融合提供了自下而上的途径，该途径导致双向融合FP，可以从低层特征图中恢复丢失的信息。 （3）进一步纯化核心功能以保留更丰富的上下文信息。自上而下和自下而上的途径中的这种核心净化只能在几次迭代中完成。 （4）将残留设计添加到核心中，导致了一个新的重核模块，该模块可以轻松训练和集成，并具有更深入或更轻的骨架。所提出的网络可在UAVDT17和MS COCO数据集上实现最新性能。代码可在https://github.com/pingyang1117/prbnet_pytorch上找到。

We present a new, embarrassingly simple approach to instance segmentation. Compared to many other dense prediction tasks, e.g., semantic segmentation, it is the arbitrary number of instances that have made instance segmentation much more challenging. In order to predict a mask for each instance, mainstream approaches either follow the "detect-then-segment" strategy (e.g., Mask R-CNN), or predict embedding vectors first then use clustering techniques to group pixels into individual instances. We view the task of instance segmentation from a completely new perspective by introducing the notion of "instance categories", which assigns categories to each pixel within an instance according to the instance's location and size, thus nicely converting instance segmentation into a single-shot classification-solvable problem. We demonstrate a much simpler and flexible instance segmentation framework with strong performance, achieving on par accuracy with Mask R-CNN and outperforming recent single-shot instance segmenters in accuracy. We hope that this simple and strong framework can serve as a baseline for many instance-level recognition tasks besides instance segmentation. Code is available at https://git.io/AdelaiDet

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.

在本文中，我们提出了简单的关注机制，我们称之为箱子。它可以实现网格特征之间的空间交互，从感兴趣的框中采样，并提高变压器的学习能力，以获得几个视觉任务。具体而言，我们呈现拳击手，短暂的框变压器，通过从输入特征映射上的参考窗口预测其转换来参加一组框。通过考虑其网格结构，拳击手通过考虑其网格结构来计算这些框的注意力。值得注意的是，Boxer-2D自然有关于其注意模块内容信息的框信息的原因，使其适用于端到端实例检测和分段任务。通过在盒注意模块中旋转的旋转的不变性，Boxer-3D能够从用于3D端到端对象检测的鸟瞰图平面产生识别信息。我们的实验表明，拟议的拳击手-2D在Coco检测中实现了更好的结果，并且在Coco实例分割上具有良好的和高度优化的掩模R-CNN可比性。 Boxer-3D已经为Waymo开放的车辆类别提供了令人信服的性能，而无需任何特定的类优化。代码将被释放。

The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems. A number of successful systems have been proposed in recent years, but apples-toapples comparisons are difficult due to different base feature extractors (e.g., VGG, Residual Networks), different default image resolutions, as well as different hardware and software platforms. We present a unified implementation of the Faster R-CNN [31], R-FCN [6] and SSD [26] systems, which we view as "meta-architectures" and trace out the speed/accuracy trade-off curve created by using alternative feature extractors and varying other critical parameters such as image size within each of these meta-architectures. On one extreme end of this spectrum where speed and memory are critical, we present a detector that achieves real time speeds and can be deployed on a mobile device. On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.