改进YOLOV3实时交通标志检测算法

交通标志检测是智能驾驶任务中的重要一环。为了满足检测精度和实时检测的要求,基于YOLOV3提出一种改进的实时交通标志检测算法。采用跨阶段局部网络作为特征提取模块,优化梯度信息,减少推理计算量;同时以路径聚合网络替代特征金字塔网络,在解决多尺度特征融合的同时,保留了更加准确的目标空间信息,提高目标检测精度;并且引入完备交并比损失函数替代均方误差损失,提高定位精度。与其他目标检测算法在CCTSDB数据集上进行对比检测,实验结果表明,改进后的算法平均精度达到95.2%,检测速度达到113.6?frame/s,与YOLOV3算法相比,分别提升2.37%和142%。     

改进YOLOv5的小目标交通标志实时检测算法

在真实场景下准确实时检测小目标交通标志对自动驾驶有重要意义,针对YOLOv5算法检测小目标交通标志精度低的问题,提出一种基于改进YOLOv5的小目标交通标志实时检测算法。借鉴跨阶段局部网络思想,在YOLOv5的空间金字塔池化上设置新的梯度路径,强化特征提取能力;在颈部特征融合中增设深、浅卷积特征的可学习自适应权重,更好地融合深层语义和浅层细节特征,提高小目标交通标志的检测精度。为验证所提算法的优越性,在TT100K交通标志数据集上进行了实验验证。实验结果表明所提算法在小目标交通标志上的平均精度均值（mean average precision,mAP）为77.3%,比原始YOLOv5提升了5.4个百分点,同时也优于SSD、RetinaNet、YOLOX、SwinTransformer等算法的检测结果。所提算法的运行速度为46.2 frame/s,满足检测实时性的要求。     

面向复杂城市交通场景的一种实时车道线检测方法

车道线检测是智能交通监控及自动驾驶的基础步骤,为提高其鲁棒性和实时性,针对复杂城市交通场景中自动驾驶需要检测车道线的需求,提出了一种实时车道线检测算法,首先运用改进灰度化变换突显车道线的特征,并通过改进的Gabor滤波算法增强车道线的边缘信息;最后采用多约束霍夫变换筛选得到平行车道线从而实现实时车道线检测。实验表明,该方法在三种不同真实的交通道路场景下,提高了车道线检测精度及处理速度,可应用于实时车道线检测系统。     

基于改进SSD的多尺度低空无人机检测

鉴于低空场景下无人机尺度多变、背景复杂的特点,提出一种基于深度学习的多尺度低空无人机目标检测算法.针对高层卷积特征对小无人机目标特征表达能力差的问题,引入VGG16低层特征图Conv3_3,构建特征金字塔网络,利用有效感受野技术,重新设计各特征图中先验框的尺寸和长宽比例,建立多尺度、多背景且包含干扰目标的低空无人机图像数据集,完成算法的训练和优化.实验结果表明,该算法相比原始SSD网络,平均精度(average precision,AP)提高了7.32％,有较好的抗干扰效果和实时检测能力.     

基于距离约束的改进FCOS遥感图像检测方法

基于深度学习的遥感图像目标检测方法通常难以排除复杂场景下的背景干扰，从而导致检测精度低。为解决该问题，设计了一种基于尺度分层的特征金字塔结构，并提出了一种基于距离约束的中心回归（distance-constraints centerness,DCCN），从而形成了基于距离约束的改进FCOS遥感图像检测方法。基于尺度分层的特征金字塔结构包括高层语义信息激活模块和低层有效特征感知模块，其中高层语义信息模块重构了特征融合阶段对高层特征图的处理方式，提升了特征金字塔顶部区域的语义感知能力，低层有效特征感知模块通过引入通道注意力机制，增强了通道间的信息交互能力。DCCN能够利用预测样本框与真实样本框之间的距离因素作为回归评估条件，提升了预测框的回归效果。在NWPU VHR-10数据集的实验中，该方法的精度达到92.6%，相比于原FCOS方法提升了4.9个百分点，有效改善了遥感图像检测的精度。     

基于卷积神经网络的交通标志检测

针对自动驾驶场景下,提高交通标志检测速度和准确率的问题,提出一种基于卷积神经网络(Convolutional Neural Network,CNN)的交通标志检测算法,与传统的图像检测算法相比拥有明显的优势。首先解析影响交通标志检测准确性的因素,并对算法提出了两项改进:使用101层的残差网络作为特征提取的基础网络以获得高精度的特征提取和物体检测,同时优化网络的区域候选框特征提取方式以提高交通标志图像的检测效果。在GTSDB德国交通标志检测基准数据集上的实验结果表明,该算法实现在复杂背景下交通标志的精准检测。     

改进YOLOv5s的交通标志识别算法

为了准确且实时地检测到交通标志指示牌, 减少交通事故的发生和推动智慧交通的发展, 针对现有的道路交通标志检测模型存在的精度不足、权重文件大、检测速度慢的问题, 设计了一种基于计算机视觉技术的改进YOLOv5s检测算法YOLOv5s-GC. 首先, 使用copy-paste进行数据增强后再送入网络进行训练, 加强对小目标的检测能力; 然后, 引入Ghost来构建网络, 削减原网络的参数和计算量, 实现轻量化模型; 最后, 将坐标注意力机制(coordinate attention)融合到骨干网络里, 增强对待测目标的表示和定位能力, 提高识别精度. 实验结果表明, YOLOv5s-GC模型相比于原YOLOv5s模型, 参数数目减少了12%, 检测速度提高了22%, 平均精度达到了94.2%, 易于部署且能满足实际自动驾驶场景中对识别交通标志的速度和准确度要求.     

基于YOLOv5的交通标志识别

交通标志识别是自动驾驶技术中的关键一部分.针对交通标志在道路场景中目标较小且识别精度较低的问题,提出一种改进的YOLOv5算法.首先在YOLOv5模型中引入全局注意力机制(GAM),提高网络捕获不同尺度交通标志特征的能力;其次将YOLOv5算法中使用的GIoU损失函数更换为更具回归特性的CIoU损失函数来优化模型,提高对交通标志的识别精度.最后在Tsinghua-Tencent 100K数据集上进行训练,实验结果表明,改进后的YOLOv5算法对交通标志识别的平均精度均值为93.00%,相比于原算法提升了5.72%,具有更好的识别性能.     

基于Darknet23和特征融合的交通标志检测方法

道路交通标志检测是智能交通的重要环节之一，针对交通标志检测存在背景复杂、目标较小、检测速度慢等问题，选取工业界青睐的YOLOv3模型提出一种改进的检测方法。利用双向特征金字塔结构实现图像低、中、高层特征语意信息的双向融合，提升低层预测目标的分类和高层预测目标的定位能力；将原模型的主干特征提取网络进行改进，提出Darknet23网络，以提高网络的提取能力和减少计算量；根据目标形状的特点，使用K-means聚类算法得到用于训练合适的锚点框，并在边框回归中引入灵活性更强的Lα-CIOU损失函数，使网络朝着预测框与真实框重叠度较高的方向去优化。实验结果表明，该方法在CCTSDB数据集上mAP@0.75达到86.10%、mAP@0.5:0.05:0.95达到70.017%，相比原网络分别提升10.17%和5.656%，参数量减少3 622 091，速度提升8.27 f/s，且优于SSD和Faster RCNN等主流的检测网络。     

基于多尺度联合权重分配的目标检测算法

针对SSD（single shot multibox detector）算法在复杂交通场景下对交通标志小目标识别效果不佳的问题,提出一种基于多尺度特征互补和重点特征信息挖掘（multi-scale feature complementary fusion and key feature information mining SSD,MK-SSD）的目标检测改进算法。利用跨阶段局部网络设计多尺度特征互补模块,同时构建多路径特征融合网络,有效提升浅层网络对小目标的特征提取能力。设计联合权重分配模块,将感知域与重点信息挖掘相结合,更高效地利用重点特征信息并抑制对非重点信息的关注度。利用轻量化残差块对预测网络进行改进,提升目标检测能力。经实验分析,改进后的算法在自制交通标志数据集上平均准确率达到89.64%,在保证实时性的同时,相较于YOLO系列和SSD系列算法拥有更高的检测精度,能检测出大部分SSD网络漏检的小目标。     

煤矿井下安全监控分站的设计及其仿真实现

为了设计一种具有低成本、低功耗、易操作、功能强且可靠性高的煤矿井下安全分站,针对煤矿安全生产实际,文章提出了采用MCS-51系列单片机为核心、具有CAN总线通信接口的煤矿井下安全监控分站的设计方案;首先给出煤矿井下安全监控分站的整体构架设计,然后着重阐述模拟量输入信号处理系统的设计过程,最后说明单片机最小系统及其键盘、显示、报警、通信等各个组成部分的设计;为验证设计方案的可行性与有效性,使用Proteus软件对设计内容进行仿真验证,设计的煤矿井下安全监控分站具有瓦斯、温度等模拟量参数超标报警功能和电机开停、风门开闭等开关量指示功能;仿真结果表明:设计的煤矿井下安全监控分站具有一定的实际应用价值.     

View-based model-driven architecture for enhancing maintainability of data access services

In modern service-oriented architectures, database access is done by a special type of services, the so-called data access services (DAS). Though, particularly in data-intensive applications, using and developing DAS are very common today, the link between the DAS and their implementation, e.g. a layer of data access objects (DAOs) encapsulating the database queries, still is not sufficiently elaborated, yet. As a result, as the number of DAS grows, finding the desired DAS for reuse and/or associated documentation can become an impossible task. In this paper we focus on bridging this gap between the DAS and their implementation by presenting a view-based, model-driven data access architecture (VMDA) managing models of the DAS, DAOs and database queries in a queryable manner. Our models support tailored views of different stakeholders and are scalable with all types of DAS implementations. In this paper we show that our view-based and model driven architecture approach can enhance software development productivity and maintainability by improving DAS documentation. Moreover, our VMDA opens a wide range of applications such as evaluating DAS usage for DAS performance optimization. Furthermore, we provide tool support and illustrate the applicability of our VMDA in a large-scale case study. Finally, we quantitatively prove that our approach performs with acceptable response times.     

A pragmatic multi-method investigation of discrepant technological events: Coping,attributions, and ‘accidental’ learning

Discrepant technological events or situations that entail a problem, a misunderstanding or a difficulty with the Information Technology (IT) being employed, are common in the workplace, and can lead to frustration and avoidance behaviors. Little is known, however, about how individuals cope with these events. This paper examines these events by using a multi-method pragmatic approach informed by coping theory. The results of two studies – a critical incident study and an experiment – serve to build and test, respectively, a theoretical model that posits that individuals use a variety of strategies when dealing with these events: they experience negative emotions, make external attributions, and adopt engagement coping strategies directed at solving the event, eventually switching to a disengagement coping strategy when they feel they have no control over the situation. Furthermore, users’ efforts may result in ‘accidental’ learning as they try to overcome the discrepant IT events through engagement coping. The paper ends with a discussion of the results in light of existing literature, future opportunities for research, and implications for practice.     

The Distributed Time-Triggered Simulation Scheme: Core Principles and Supporting Execution Engine

Distributed real-time simulation is a young technology field but its practice is under increasing demands. In recent years the author and his collaborators have been establishing a new approach called the distributed time-triggered simulation (DTS) scheme which is conceptually simple and easy to use but widely applicable. The concept was initiated in the course of developing a new-generation object-oriented real-time programming scheme called the time-triggered message-triggered object (TMO) programming scheme. Some fundamental issues inherent in distributed real-time simulation that were learned during recent experimental studies are discussed along with some approaches for resolving the issues. An execution engine developed to support both the TMOs engaged in control computation and the TMOs engaged in DTS is also discussed along with its possible extensions that will enable significantly larger-scale DTSs.     

Software-Konfigurationsmanagement in großen Softwareprojekten

Zusammenfassung Mit zunehmender Größe der Softwaresysteme verschärfen sich die für die Software-Herstellung typischen Probleme: Beherrschen großer Objektmengen, Erhalten der Systemkonsistenz, Kontrolle der ständigen Änderungseinflüsse und Gewährleisten einer langen Lebensdauer. Die Disziplin Konfigurationsmanagement bildet den methodischen Ansatz, diese Probleme besser zu beherrschen. Software-Konfigurationsmanagement faßt die Herstellung von Softwaresystemen als eine Abfolge von kontrollierten Änderungen an gesicherten Zwischen- und Endergebnissen auf. Dargestellt werden die Objekte und Funktionen des Software-Konfigurationsmanagements sowie die hierfür in großen Software-Projekten benötigten Methoden, Instanzen und Hilfsmittel.     

Information for Authors

正http://www.zju.edu.cn/jzus http://www.springerlink.com Aim The Journals of Zhejiang University-SCIENCE(A/B/C)are edited by the international board of distinguished Chinese and foreign scientists,and are aimed to present the latest developments and achievements in scientific research in China and     

Editorial:Brain-machine interface(BMI) and cyborg intelligence

正Brain-machine interfaces(BMIs)aim at building a direct communication pathway between the brain and an external device,and represent an area of research where significant progress has been made during the past decade.Based on BMIs,mind information can be read out by neural signals to control     

Information for Authors

正http://www.zju.edu.cn/jzus http://www.springerlink.com Aim The Journals of Zhejiang University-SCIENCE(A/B/C)are edited by the international board of distinguished Chinese and foreign scientists,and are aimed to present the latest developments and achievements in scientific research in China and overseas to the world’s scientific circles,especially to stimulate     

SCIENCE CHINA Information Sciences SUBJECT INDEX TO VOLUME 57(2014)

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Information for Authors

正http://www.zju.edu.cn/jzus http://www.springerlink.com Aim The Journals of Zhejiang University-SCIENCE(A/B/C)are edited by the international board of distinguished Chinese and foreign scientists,and are aimed to present the latest developments and achievements in scientific research in China and overseas to the world’s scientific circles,especially to stimulate