尽管Yolov2方法在对象检测时非常快，但由于其骨干网络的性能较低和多尺度区域特征的缺乏，其检测准确性受到限制。因此，在本文中提出了一种基于Yolov2的Yolo（DC）Yolo（DC-SPP-YOLO）方法的密集连接（DC）和空间金字塔池（SPP）方法。具体而言，在Yolov2的骨干网络中采用了卷积层的密集连接，以增强特征提取并减轻消失的梯度问题。此外，引入了改进的空间金字塔池以池并加入多尺度区域特征，以便网络可以更全面地学习对象功能。 DC-SPP-YOLO模型是根据由MSE（均方误差）损耗和跨透镜损失组成的新损失函数建立和训练的。实验结果表明，DC-SPP-Yolo的地图（平均平均精度）高于Pascal VOC数据集和UA-Detrac数据集上的Yolov2。提出了DC-SPP-Yolo方法的有效性。

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.

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 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 .

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.

最近已经设计了一些轻巧的卷积神经网络（CNN）模型，用于遥感对象检测（RSOD）。但是，他们中的大多数只是用可分离的卷积代替了香草卷积，这可能是由于很多精确损失而无法有效的，并且可能无法检测到方向的边界框（OBB）。同样，现有的OBB检测方法很难准确限制CNN预测的对象的形状。在本文中，我们提出了一个有效的面向轻质对象检测器（LO-DET）。具体而言，通道分离聚集（CSA）结构旨在简化可分开的卷积的复杂性，并开发了动态的接收场（DRF）机制，以通过自定义卷积内核及其感知范围来保持高精度，以保持高精度。网络复杂性。 CSA-DRF组件在保持高精度的同时优化了效率。然后，对角支撑约束头（DSC-Head）组件旨在检测OBB，并更准确，更稳定地限制其形状。公共数据集上的广泛实验表明，即使在嵌入式设备上，拟议的LO-DET也可以非常快地运行，具有检测方向对象的竞争精度。

Object detection, one of the three main tasks of computer vision, has been used in various applications. The main process is to use deep neural networks to extract the features of an image and then use the features to identify the class and location of an object. Therefore, the main direction to improve the accuracy of object detection tasks is to improve the neural network to extract features better. In this paper, I propose a convolutional module with a transformer[1], which aims to improve the recognition accuracy of the model by fusing the detailed features extracted by CNN[2] with the global features extracted by a transformer and significantly reduce the computational effort of the transformer module by deflating the feature mAP. The main execution steps are convolutional downsampling to reduce the feature map size, then self-attention calculation and upsampling, and finally concatenation with the initial input. In the experimental part, after splicing the block to the end of YOLOv5n[3] and training 300 epochs on the coco dataset, the mAP improved by 1.7% compared with the previous YOLOv5n, and the mAP curve did not show any saturation phenomenon, so there is still potential for improvement. After 100 rounds of training on the Pascal VOC dataset, the accuracy of the results reached 81%, which is 4.6 better than the faster RCNN[4] using resnet101[5] as the backbone, but the number of parameters is less than one-twentieth of it.

In recent years, object detection has achieved a very large performance improvement, but the detection result of small objects is still not very satisfactory. This work proposes a strategy based on feature fusion and dilated convolution that employs dilated convolution to broaden the receptive field of feature maps at various scales in order to address this issue. On the one hand, it can improve the detection accuracy of larger objects. On the other hand, it provides more contextual information for small objects, which is beneficial to improving the detection accuracy of small objects. The shallow semantic information of small objects is obtained by filtering out the noise in the feature map, and the feature information of more small objects is preserved by using multi-scale fusion feature module and attention mechanism. The fusion of these shallow feature information and deep semantic information can generate richer feature maps for small object detection. Experiments show that this method can have higher accuracy than the traditional YOLOv3 network in the detection of small objects and occluded objects. In addition, we achieve 32.8\% Mean Average Precision on the detection of small objects on MS COCO2017 test set. For 640*640 input, this method has 88.76\% mAP on the PASCAL VOC2012 dataset.

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。

X射线图像在制造业的质量保证中起着重要作用，因为它可以反映焊接区域的内部条件。然而，不同缺陷类型的形状和规模大大变化，这使得模型检测焊接缺陷的挑战性。在本文中，我们提出了一种基于卷积神经网络的焊接缺陷检测方法，即打火机和更快的YOLO（LF-YOLO）。具体地，增强的多尺度特征（RMF）模块旨在实现基于参数和无参数的多尺度信息提取操作。 RMF使得提取的特征映射能够代表更丰富的信息，该信息是通过卓越的层级融合结构实现的。为了提高检测网络的性能，我们提出了一个有效的特征提取（EFE）模块。 EFE处理具有极低消耗量的输入数据，并提高了实际行业中整个网络的实用性。实验结果表明，我们的焊接缺陷检测网络在性能和消耗之间实现了令人满意的平衡，达到92.9平均平均精度MAP50，每秒61.5帧（FPS）。为了进一步证明我们方法的能力，我们在公共数据集MS Coco上测试它，结果表明我们的LF-YOLO具有出色的多功能性检测性能。代码可在https://github.com/lmomoy/lf-yolo上获得。

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.

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224×224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-theart classification results using a single full-image representation and no fine-tuning.The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102× faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007.In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.

We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prior work. The improved model, YOLOv2, is state-of-the-art on standard detection tasks like PASCAL VOC and COCO. Using a novel, multi-scale training method the same YOLOv2 model can run at varying sizes, offering an easy tradeoff between speed and accuracy. At 67 FPS, YOLOv2 gets 76.8 mAP on VOC 2007. At 40 FPS, YOLOv2 gets 78.6 mAP, outperforming state-of-the-art methods like Faster R-CNN with ResNet and SSD while still running significantly faster. Finally we propose a method to jointly train on object detection and classification. Using this method we train YOLO9000 simultaneously on the COCO detection dataset and the ImageNet classification dataset. Our joint training allows YOLO9000 to predict detections for object classes that don't have labelled detection data. We validate our approach on the ImageNet detection task. YOLO9000 gets 19.7 mAP on the ImageNet detection validation set despite only having detection data for 44 of the 200 classes. On the 156 classes not in COCO, YOLO9000 gets 16.0 mAP. But YOLO can detect more than just 200 classes; it predicts detections for more than 9000 different object categories. And it still runs in real-time.

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. In this work, we introduce two new modules to enhance the transformation modeling capability of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from the target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the performance of our approach. For the first time, we show that learning dense spatial transformation in deep CNNs is effective for sophisticated vision tasks such as object detection and semantic segmentation. The code is released at https://github.com/ msracver/Deformable-ConvNets.

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。

Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.

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.

从卷积神经网络的快速发展中受益，汽车牌照检测和识别的性能得到了很大的改善。但是，大多数现有方法分别解决了检测和识别问题，并专注于特定方案，这阻碍了现实世界应用的部署。为了克服这些挑战，我们提出了一个有效而准确的框架，以同时解决车牌检测和识别任务。这是一个轻巧且统一的深神经网络，可以实时优化端到端。具体而言，对于不受约束的场景，采用了无锚方法来有效检测车牌的边界框和四个角，这些框用于提取和纠正目标区域特征。然后，新型的卷积神经网络分支旨在进一步提取角色的特征而不分割。最后，将识别任务视为序列标记问题，这些问题通过连接派时间分类（CTC）解决。选择了几个公共数据集，包括在各种条件下从不同方案中收集的图像进行评估。实验结果表明，所提出的方法在速度和精度上都显着优于先前的最新方法。

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。

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.