基于距离交并比回归的孪生网络目标跟踪算法

为解决视频目标跟踪过程中目标框和预测框边界不重叠情况下无法优化的问题,提出了一种距离交并比(distance intersection over union, DIOU)回归的孪生网络目标跟踪算法.通过孪生网络和区域建议网络(region proposal network, RPN)保持实时效果,将距离交并比引入回归分支,结合重叠率和中心点距离构建损失度量,加快模型训练的收敛速度,为边界框提供更直接的回归方向.同时,使用Resnet50网络替换SiamRPN网络的特征提取网络,进一步提高目标特征表示的准确性.实验结果表明,DIOU回归损失在视频目标尺度变化、低分辨率、光照变化等干扰情况下,具有较强鲁棒性.     

基于深度学习的多媒体移动物体检测技术研究

为了解决深度学习技术无法满足用户对多张照片移动目标物体识别的需求,提出基于深度学习的多媒体移动物体检测技术.提出基于前馈神经网络的目标定位技术,设计目标定位模型,计算目标边界框的大致位置以及概率;对视频中的目标行为进行预测;利用卷积神经网络融合特征和自然语言搜索特征完成自然目标搜索.     

基于深度学习的交通标志检测方法研究

提出了一种基于Faster R-CNN深度学习框架的交通标志检测方法,使用VGG16卷积神经网络自动提取交通标志图像特征,并将卷积特征图传入区域建议网络(RPN)中进行前景目标筛选及回归目标边框,将建议区域框映射到特征图上,经过RoI池化层后输出固定大小的建议框,利用分类网络对建议区域进行具体的类别判断,并精确回归目标的边框。并将算法在德国交通标志数据集GTSDB进行了实验验证,实验结果表明了所提出算法的有效性,该方法对不同光照、遮挡、标志模糊等具有鲁棒性。     

一种基于Faster R-CNN的车辆检测算法

针对传统的车辆检测算法无法自适应地完成在复杂场景变化下提取目标相应特征的现象,提出了一种基于深度学习的车辆检测算法,该算法结合了Faster R-CNN开源框架和Loc Net网络算法。首先,利用RPN算法获得图片中的候选区域,以减少检测过程中对每张图片的计算量;然后,进入Fast R-CNN网络,利用该深度网络中的卷积层和池化层,自适应地获得车辆目标的所有特征;最后,进入Loc Net网络,通过输入已经得到的图片候选区域,通过卷积层和池化层,不断计算候选区域边界的概率,达到不断优化候选区域边界,最后得到车辆目标的边界框。使用深度学习卷积神经网络,可以避免人工设计车辆目标特征适用性不广泛的缺点,提升车辆目标检测和定位的准确性。     

面向内河航运安全监控的多尺度船舶图像目标识别方法

针对YOLOv3算法中IoU边界框回归损失函数对小尺度船舶预测框容易产生误识及漏识问题,利用目标预测框与真实框的最小闭包区域和关键点的归一化距离,提出一种新的损失函数MIoU,该损失函数可显著提升多尺度船舶目标预测框的回归速度及精度.实验表明:提出的YOLOv3-MIoU算法对六类船舶的识别精度均超过97％,mAP值达...     

基于Mask R-CNN的荧光编码微球图像检测方法

为降低荧光编码微球技术的应用成本,提出了一种基于Mask R-CNN目标检测算法的荧光编码微球图像检测方法.首先基于TensorFlow和Keras深度学习框架搭建Mask R-CNN网络模型,整体网络由特征提取网络,候选区域生成网络和分支处理网络3部分构成;通过有标注定性图像样本集训练网络模型,并使用合成图像实现训练集数据增强;将待检测定性图像样本输入训练完成的网络模型获得定性图像的语义掩膜.实验结果表明,对于单色和双色微球定性实验图像,平均检测准确度分别达94.17%和95.96%,可实现荧光编码微球定性图像的边界框检测、分类以及语义掩膜生成.     

基于堆叠去噪自编码器算法的穿墙人体检测（英文）

超宽带雷达在穿墙人体检测中的应用已经越来越成熟,将堆叠去噪自编码器算法应用于穿墙人体状态的识别和分类中,首先使用无监督学习方法对自编码器网络进行训练,从而获得原始数据更加抽象的特征表示;然后在堆叠去噪自编码器网络的最后一层添加一个分类器.使用有监督的学习方法对网络进行微调,获得最优化的模型;最后,将测试集输入到已经训练好的网络模型上进行测试.实验结果表明,堆叠去噪自编码器深度网络可以对穿墙人体目标状态进行有效地分类识别.     

基于YOLOv5和U-Net3+的桥梁裂缝智能识别与测量

为了克服传统数字图像处理方法进行桥梁裂缝识别时面临的效率低、效果不佳等问题,提出了集成深度学习YOLOv5和U-Net3+算法的一体化桥梁裂缝智能检测方法.通过调整算法宽度和深度参数,优化边界框损失函数,构建基于YOLOv5目标检测算法的裂缝识别定位模型,实现桥梁裂缝快速识别与定位;引入结合深度监督策略及预测输出模块的U-Net3+图像分割算法,训练并构建桥梁裂缝高效分割模型,实现像素级裂缝智能化提取;建立结合连通域去噪、边缘检测、形态学处理的八方向裂缝宽度测量法,基于U-Net3+裂缝分割结果实现裂缝形态及宽度高精度测量;利用LabelImg图像标注软件制作包含4 414张图像的裂缝识别定位模型训练数据集;利用LabelImg图像标注软件及CFD数据集制作包含908张图像的裂缝分割模型训练数据集;利用无人机航拍的485张5 280×2 970 pixels桥梁索塔裂缝图像,来制作裂缝智能检测模型的测试对象.将所提出的裂缝检测方法应用于上述裂缝测试对象,其裂缝识别定位准确率91.55%、召回率95.15%、F1分数93.32%,裂缝分割准确率93.02%、召回率92.22%、F1分数92.22%.结果表明,基于YOLOv5与U-Net3+的桥梁裂缝智能检测方法,可实现桥梁裂缝高效率、高精度、智能化检测,具有较强的研究价值和广泛的应用前景.     

一种融合表观与属性信息的车辆重识别方法

针对基于深度学习的车辆重识别模型缺乏提取车辆局部细节描述的能力,以及不同环境下同一车辆表观特征变化显著,增加车辆重识别的难度的问题,提出一种融合多尺度车辆表观和属性信息的重识别模型.充分利用车辆颜色和车型属性在不同环境下较为稳定且易判断的特性,将其融合到车辆表观特征中,获得强化提升的深度特征;另外使用交叉熵损失函数与Triplet损失函数构建多任务的目标优化函数对模型进行参数训练.该方法在VeRi-776车辆数据库上的实验结果表明:通过融合颜色和车型属性特征可以明显地提高车辆重识别的准确率,并可以取得优于其他大部分对比方法的性能.     

基于残差网络的配电柜设备元件状态识别

随着工业智能巡检的不断发展,基于数字图像处理方法的设备元件状态识别系统被广泛应用.为提升配电室中配电柜设备元件状态识别的准确率,提出了一种基于残差网络(residual networks, ResNet)的设备元件状态识别方法.首先搭建数据采集系统,构建数据集;然后对配电柜图像,裁剪预设的设备元件目标区域,生成设备元件图像;对于设备元件图像,构建基于ResNet的元件状态识别模型并训练;使用训练完毕的模型识别元件的状态.以变电站配电室中配电柜设备元件数据集作为研究对象,对于特征复杂的元件采用单预测头的网络,对于特征简单的元件采用多预测头的网络;然后使用紧凑和剪枝的模型压缩方法在精度损失较小的情况下减少参数量和计算量;最后介绍巡检系统的架构设计,将JetSon Nano边缘终端作为算法模块的运行硬件,以减少通信成本.     

Characterization of Ordovician carbonate reservoirs, southeastern Saskatchewan, Canada

The discovery of the prolific Ordovician Red River reservoirs in 1995 in southeastern Saskatchewan was the catalyst for extensive exploration activity which resulted in the discovery of more than 15 new Red River pools. The best yields of Red River production to date have been from dolomite reservoirs. Understanding the processes of dolomitization is, therefore, crucial for the prediction of the connectivity, spatial distribution and heterogeneity of dolomite reservoirs.The Red River reservoirs in the Midale area consist of 3～4 thin dolomitized zones, with a total thickness of about 20 m, which occur at the top of the Yeoman Formation. Two types of replacement dolomite were recognized in the Red River reservoir: dolomitized burrow infills and dolomitized host matrix. The spatial distribution of dolomite suggests that burrowing organisms played an important role in facilitating the fluid flow in the backfilled sediments. This resulted in penecontemporaneous dolomitization of burrow infills by normal seawater. The dolomite in the host matrix is interpreted as having occurred at shallow burial by evaporitic seawater during precipitation of Lake Almar anhydrite that immediately overlies the Yeoman Formation. However, the low δ18O values of dolomited burrow infills (-5.9‰～ -7.8‰, PDB) and matrix dolomites (-6.6‰～ -8.1‰, avg. -7.4‰ PDB) compared to the estimated values for the late Ordovician marine dolomite could be attributed to modification and alteration of dolomite at higher temperatures during deeper burial, which could also be responsible for its 87Sr/86Sr ratios (0.7084～0.7088) that are higher than suggested for the late Ordovician seawaters (0.7078～0.7080). The trace amounts of saddle dolomite cement in the Red River carbonates are probably related to "cannibalization" of earlier replacement dolomite during the chemical compaction.     

A computer generator for randomly layered structures

AcomputergeneratorforrandomlylayeredstructuresYUJia shun1,2,HEZhen hua2(1.TheInstituteofGeologicalandNuclearSciences,NewZealand;2.StateKeyLaboratoryofOilandGasReservoirGeologyandExploitation,ChengduUniversityofTechnology,China)Abstract:Analgorithmisintrod…     

海南岛地体及其毗邻陆缘晚中生代—新生代古地磁研究和构造演化

本文叙述了对海南岛及其毗邻大陆边缘白垩纪到第四纪地层岩石进行古地磁研究的全部工作过程。通过分析岩石中剩余磁矢量的磁偏角及磁倾角的变化,提出海南岛白垩纪以来经历的构造演化模式如下:早期伴随顺时针旋转而向南迁移,后期伴随逆时针转动并向北运移。联系该地区及邻区的地质、地球物理资料,对海南岛上述的构造地体运动提出以下认识:北部湾内早期有一拉张作用,主要是该作用使湾内地壳显著伸长减薄,形成北部湾盆地。从而导致了海南岛的早期构造运动,而海南岛后期的构造运动则主要是受南海海底扩张的影响。海南地体运动规律的阐明对于了解北部湾油气盆地的形成演化有重要的理论和实际意义。     

Exciton Seebeck in Molecular Systems

Various applications relevant to the exciton dynamics,such as the organic solar cell,the large-area organic light-emitting diodes and the thermoelectricity,are operating under temperature gradient.The potential abnormal behavior of the exicton dynamics driven by the temperature difference may affect the efficiency and performance of the corresponding devices.In the above situations,the exciton dynamics under temperature difference is mixed with     

Achieving Unusual States of Matter Under High Pressure

The elongation method,originally proposed by Imamura was further developed for many years in our group.As a method towards O(N)with high efficiency and high accuracy for any dimensional systems.This treatment designed for one-dimensional(ID)polymers is now available for three-dimensional(3D)systems,but geometry optimization is now possible only for 1D-systems.As an approach toward post-Hartree-Fock,it was also extended to     

《Chinese Science Bulletin》SUBJECT INDEX TO VOLUME 59(2014)

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An on-line scaling method for improving scalability of a database cluster

The explosive growth of the Internet and database applications has driven database to be more scalable and available, and able to support on-line scaling without interrupting service. To support more client's queries without downtime and degrading the response time, more nodes have to be scaled up while the database is running. This paper presents the overview of scalable and available database that satisfies the above characteristics. And we propose a novel on-line scaling method. Our method improves the existing on-line scaling method for fast response time and higher throughputs. Our proposed method reduces unnecessary network use, i.e. , we decrease the number of data copy by reusing the backup data. Also, our on-line scaling operation can be processed parallel by selecting adequate nodes as new node. Our performance study shows that our method results in significant reduction in data copy time.     

An improved R-tree based on childnode''s probability

R-Tree is a good structure for spatial searching. But in this indexing structure,either the sequence of nodes in the same level or sequence of traveling these nodes when queries are made is random. Since the possibility that the object appears in different MBR which have the same parents node is different, if we make the subnode who has the most possibility be traveled first, the time cost will be decreased in most of the cases. In some case, the possibility of a point belong to a rectangle will shows direct proportion with the size of the rectangle. But this conclusion is based on an assumption that the objects are symmetrically distributing in the area and this assumption is not always coming into existence. Now we found a more direct parameter to scale the possibility and made a little change on the structure of R-tree, to increase the possibility of founding the satisfying answer in the front sub trees. We names this structure probability based arranged R-tree (PBAR-tree).     

Integrating GIS Web services based on mediating architecture

The geographic information service is enabled by the advancements in general Web service technology and the focused efforts of the OGC in defining XML-based Web GIS service. Based on these models, this paper addresses the issue of services chaining,the process of combining or pipelining results from several interoperable GIS Web Services to create a customized solution. This paper presents a mediated chaining architecture in which a specific service takes responsibility for performing the process that describes a service chain. We designed the Spatial Information Process Language (SIPL) for dynamic modeling and describing the service chain, also a prototype of the Spatial Information Process Execution Engine (SIPEE) is implemented for executing processes written in SIPL. Discussion of measures to improve the functionality and performance of such system will be included.     

Tracking data management using topology of moving object database system for moving object constrained trajectory in LBS application

Advances in wireless technologies and positioning technologies and spread of wireless devices, an interest in LBS (Location Based Service) is arising. To provide location based service, tracking data should have been stored in moving object database management system (called MODBMS) with proper policies and managed efficiently. So the methods which acquire the location information at regular time intervals then, store and manage have been studied. In this paper, we suggest tracking data management techniques using topology that is corresponding to the moving path of moving object. In our techniques, we update the MODBMS when moving object arrived at a street intersection or a curved road which is represented as the node in topology and predict the location at past and future with attribute of topology and linear function. In this technique, location data that are corresponding to the node in topology are stored, thus reduce the number of update and amount of data. Also in case predicting the location,because topology are used as well as existing location information, accuracy for prediction is increased than applying linear function or spline function.