基于云计算的多特征疲劳驾驶检测系统研究与设计

随着私家车的普及,人们对汽车安全性、舒适性要求不断提高,通过对当前车载系统分析和汽车驾驶员疲劳驾驶状态研究,提出了一种基于信息融合的多特征疲劳驾驶检测方案。方案采用高性能嵌入式系统平台与云计算相结合的方式,首先,通过嵌入式系统采集驾驶员面部图像;然后,将数据传输到Face+ 云计算平台,分析当前驾驶人员身份、年龄与微笑程度;最后,采用数字图像处理技术计算驾驶员头部位移以及统计眼睛眨动规律,综合三种指标预测驾驶员是否处于疲劳状态,实时监测驾驶员驾驶全过程。当检测到驾驶员处于疲劳驾驶状态,则通过语音的方式提醒驾驶员注意行车安全、谨慎驾驶。测试结果表明：该方案检测精度高、实时性强,并且易于和车载系统整合并推广使用。     

基于多疲劳信息融合的车载疲劳检测系统的研究和实现

疲劳驾驶是目前道路交通的一个重要安全隐患,对车载疲劳驾驶系统的研发具有重要的应用价值和广阔的市场前景。目前存在的疲劳监测系统普遍存在成本高,可靠性不足,使用不够便捷等缺陷。通过在安卓平台上采用计算机视觉的途径进行开发和实现,将大大降低系统的成本和使用复杂度。通过多种疲劳特征融合的方法对疲劳状态进行综合性判断,可以有效的增加系统的准确性和可靠性。采用优化的二叉树支持向量机多分类算法能够使得特征融合的过程具有准确性和速度上的优势。在对该疲劳检测系统的相应测试中也获取了良好的实验结果。     

驾驶员疲劳检测系统设计

以DM642为主控芯片设计了一套驾驶员疲劳检测的硬件系统,包括主控器模块、视频采集模块、视频输出模块和报警模块等相关电路;综合国内外的研究现状,确定了了疲劳状态判断的理论基础;交叉运用图像处理技术、人脸检测技术和PERCLOS疲劳检测方法,根据眼睛的疲劳特征,实时判断驾驶员的疲劳状态,进行报警,有效防止交通事故的发生;经过对系统的软硬件测试,结果表明,该方案可以有效地识别出驾驶员的疲劳状态,运行速度快、实时性好,具有较高的鲁棒性。     

红外图像的疲劳状态识别方法

目的：为了解决光照变化对疲劳检测系统造成的准确性不高的问题,提出了一种近红外环境下判断人眼状态的方法,即针对红外光补图像的人眼状态判断。方法：首先,利用Adaboost算法对人眼区域进行定位,在网格法标记人眼瞳孔部分的基础上,进行Retinex红外图像增强。接着,对二值化与边缘检测后的红外图像分别进行网格法闭合度计算,得到人眼闭合度。最后,根据闭合度计算结果设定双阈值并结合PERCLOS来判断眼睛状态。结果：在DM642硬件平台上进行疲劳检测试验,实验结果表明,人眼状态识别率达到了90%以上,且平均每秒能处理21帧图片。结论：证明了该方法不仅能有效解决光照变化带来的问题,而且满足疲劳状态检测系统的快速性、可靠性和有效性等要求。     

基于CAN总线的抬头显示系统研究

车载抬头显示系统是使驾驶员在不移动驾驶视线的情况下就能读取车辆重要信息的装置,它有助于提高驾驶员对信息的读取速度、降低驾驶疲劳、提高驾驶安全性。本文结合国内外学者的研究,针对已上市HUD显示内容格式、布置方式、辨识度、实时性等方面的问题,提出了用彩色符号、分区域、亮度可调、人机交互的显示设计原则。以问卷调查分析结果和信息的重要程度为依据,以CAN总线技术、微处理器控制技术为基础,设计了一套HUD显示系统。结果表明采用分区显示的方法辨识度高、对驾驶员视线影响较小,有助于提高信息的易读性;对显示实时性进行测试表明该装置实时性强,有效提高了驾驶安全性。     

结合几何代数改进的SIFT桥梁裂缝图像拼接算法

针对桥梁裂缝图像精度要求高,拼接质量受原图像亮度变化大、噪声干扰严重和对比度低的影响,提出了一种结合几何代数改进的SIFT桥梁裂缝图像的新型拼接算法。对SIFT算法进行了两方面的改进:一是通过几何代数空间的表示形式提取了待拼接图像的色度图像,克服了SIFT算法中色度信息丢失的不足;二是改进了SIFT算法对灰度图像建立尺度空间的方法,构建了新的可适用于多光谱图像的高斯滤波和卷积运算,确定了尺度空间。通过几何代数DoG空间检测特征点并进行预匹配。使用改进的RANSAC算法对匹配结果进行修正,完成了图像之间的精确拼接。实验结果表明,所提算法的性能优于SIFT算法,提取的特征点对数量提高了近10%;拼接过程中未产生位错现象,最终拼接结果满足桥梁裂缝图像的精度要求。     

应用于微靶装配的显微视觉检测技术

针对如何实现微小尺度（μm～mm）靶零件装配过程中的在线检测，研制了应用于微靶装配的显微视觉检测系统，并对系统的构成与显微成像、图像预处理、特征检测等关键图像处理技术进行了详细论述。提出了针对微靶零件的新型多重滤波除噪算法和几何特征检测算法，并以铝台阶、柱腔等微靶零件为应用实例开展了实验研究。结果表明，该系统采用的算法能有效、快速、准确地对靶零件进行几何特征和实时位置检测，在检测视场为3mm时，其检测精度≤3μm，角度检测精度≤0．1°，适用于ICF微靶等微型器件的微装配。     

高分辨率合成孔径雷达图像舰船目标几何特征提取方法

针对高分辨率合成孔径雷达图像设计了一种舰船目标几何特征提取算法.通过视觉注意机制检测目标区域的算法,通过频谱残差视觉显著计算模型求取显著图,完成显著区域的检测以实现舰船目标的初步定位,基于获得的视觉显著图采用最大熵算法完成阈值分割筛选出舰船区域.在提取的舰船切片的基础上,采用针对几何特征的提取算法,经图像预处理、方位角估计、旋转获取最佳表征舰船目标几何轮廓的外接矩形,相对有效准确地提取几何特征;最后,采用典型的TerraSAR-X数据进行仿真实验.结果表明,与传统方法相比,本文提出的频谱残差视觉模型完成合成孔径雷达图像舰船切片的区域分割能够有效降低虚警率,舰船目标的检测速度提高了25%~50%.该方法能够快速稳定地提取舰船目标的几何特征,也更加符合实际高分辨率图像舰船目标检测的应用需求.     

结合图像语义分割的增强现实型平视显示系统设计与研究

为了提高驾驶员在车辆行驶过程中的安全性,设计了一种结合图像语义分割的增强现实型平视显示(ARHUD)系统。首先,提出一种改进的单发多框检测器网络对道路场景图像进行语义分割,网络前端采用VGG-16提取图像特征,网络后端对获取的特征图进行上采样,从而对特征图进行像素分割。通过对网络的训练,得到场景目标的像素级分类结果,即环境的语义内容信息。随后,通过分析真实场景、光学显示系统、驾驶员之间的关系,将计算机产生的虚拟信息叠加到真实场景,并将显示内容注册到驾驶员视野中,从而提高行车安全。实验结果表明,语义分割算法的准确率能达到77.8%,虚实注册算法处理每帧图像的时间平均为45ms,约22frame·s-1。     

驾驶员疲劳态下脑氧饱和度的近红外光谱法检测及其分析

应用近红外光谱法,研究分析汽车驾驶员疲劳态下的脑氧饱和度,探讨疲劳机理,为驾驶疲劳及时预警提供新的监测思路。参试人员20名,分为两组(各10名),一组为实验组,一组为对照组。实验组执行汽车模拟驾驶作业3 h,对照组坐姿下正常休息3 h。应用近红外光谱法监测试验人员作业前后脑氧饱和度。经方差分析发现,实验组人员模拟驾驶后脑氧饱和度(rSO₂)显著性下降(p<0.01),反应时间明显延长;对照组rSO₂水平下降不显著(p>0.05)。脑氧饱和度水平(rSO₂)与驾驶疲劳存在密切相关性。近红外光谱法监测脑氧具有连续、实时、无创的特点,受外界干扰小。近红外光谱法监测驾驶员脑氧信息是预测驾驶疲劳的一种可行的方法。     

Research on Driving Fatigue Alleviation Using Interesting Auditory Stimulation Based on VMD-MMSE

The accurate detection and alleviation of driving fatigue are of great significance to traffic safety. In this study, we tried to apply the modified multi-scale entropy (MMSE) approach, based on variational mode decomposition (VMD), to driving fatigue detection. Firstly, the VMD was used to decompose EEG into multiple intrinsic mode functions (IMFs), then the best IMFs and scale factors were selected using the least square method (LSM). Finally, the MMSE features were extracted. Compared with the traditional sample entropy (SampEn), the VMD-MMSE method can identify the characteristics of driving fatigue more effectively. The VMD-MMSE characteristics combined with a subjective questionnaire (SQ) were used to analyze the change trends of driving fatigue under two driving modes: normal driving mode and interesting auditory stimulation mode. The results show that the interesting auditory stimulation method adopted in this paper can effectively relieve driving fatigue. In addition, the interesting auditory stimulation method, which simply involves playing interesting auditory information on the vehicle-mounted player, can effectively relieve driving fatigue. Compared with traditional driving fatigue-relieving methods, such as sleeping and drinking coffee, this interesting auditory stimulation method can relieve fatigue in real-time when the driver is driving normally.     

Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals

Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver’s attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain’s information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain’s local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue.     

利用几何特性及神经网络进行人脸探测技术的研究

在人脸识别过程中 ,首先也是最重要的一个环节是人脸探测 ,因为一旦从图像中定位并提取到了人脸 ,那么下一步的人脸识别工作就变得非常容易。眼睛是人脸图像中最容易探测的部位 ,而且通过探测双眼来发现人脸最符合人的视觉习惯。提出了一种基于几何特征分析和人工神经网络的由粗到细的两级人脸探测方法。在第一级中 ,眼睛和脸是通过测量眼睛的尺寸和眼睛与脸的位置关系探测到的 ,第一级的输出是一个尺寸归一化的人脸 ,但偶尔也伴随着一个或多个因对复杂背景中与眼睛类似的物体的误判而得到的非人脸图像 ;第二级神经网络正是用来过滤掉第一级中被误判的人脸。实验表明 ,这种由粗到细的两级人脸探测系统具有很高的稳定性和探测正确率     

Fatigue Detection with Spatial-Temporal Fusion Method on Covariance Manifolds of Electroencephalography

With the increasing pressure of current life, fatigue caused by high-pressure work has deeply affected people and even threatened their lives. In particular, fatigue driving has become a leading cause of traffic accidents and deaths. This paper investigates electroencephalography (EEG)-based fatigue detection for driving by mining the latent information through the spatial-temporal changes in the relations between EEG channels. First, EEG data are partitioned into several segments to calculate the covariance matrices of each segment, and then we feed these matrices into a recurrent neural network to obtain high-level temporal information. Second, the covariance matrices of whole signals are leveraged to extract two kinds of spatial features, which will be fused with temporal characteristics to obtain comprehensive spatial-temporal information. Experiments on an open benchmark showed that our method achieved an excellent classification accuracy of 93.834% and performed better than several novel methods. These experimental results indicate that our method enables better reliability and feasibility in the detection of fatigued driving.     

A scanning distortion correction method based on Ⅹ–Y galvanometer Lidar system

Aiming at the problem of scanning distortion in X-Y galvanometer light detecting and ranging(Lidar) scanning system,we propose a method of image scanning distortion correction with controllable driving voltage compensation.Firstly,the geometrical optics vectors model is established to explain the principle of pincushion distortion in the galvanometer scanning system,and the simulation result of scanning trajectory is consistent with experiments.The linear relationship between the driving voltage and the scanning angle of the galvanometer is verified.Secondly,the relationship between the deflection angle of the galvanometer and the scanning trajectory and the driving voltage is deduced respectively,and an image scanning correction algorithm with controllable driving voltage compensation is obtained.The simulation experiment results of the proposed method show that the root-mean-square error(RMSE) and the corresponding curve between the scan value and the actual value at different distances,have a good correction effect for the pincushion distortion.Finally,the X-Y galvanometer scanning Lidar system is established to obtain undistorted two-dimensional scanned image and it can be applied to the three-dimensional Lidar scanning system in the actual experiments,which further demonstrates the feasibility and practicability of our method.     

A simple filter for detecting low-rank submatrices

We present a simple algorithm for detecting low-rank submatrices from within a much larger matrix. This algorithm relies on a basic geometric property of high-dimensional space: random 2-d projections of eccentric gaussian distributions are typically concentrated in opposite quadrants of the plane.     

基于四象限探测器的激光导引镜头的研制

为了满足激光制导对大视场、高线性度探测的性能要求,基于激光制导炸弹的应用需求,介绍了四象限探测器的工作原理和特点,分析了光斑大小、能量均匀性、线性度、探测距离等参数对探测精度的影响;结合系统性能指标,选择了合理的光学系统结构类型,完成了光学系统设计和光机结构设计;利用畸变、点列图、足迹图、能量集中度等指标对系统性能进行了评价,并分析了目标大小和探测距离对光斑大小的影响。测试结果表明,激光导引镜头总视场为±20°、线性视场为±10°、目标大小为1.5~2.4 m、探测距离为50 m~4 km、测角精度优于0.2°,能够满足激光导引的需求。     

Transitionless driving on local adiabatic quantum search algorithm

We apply the transitionless driving on the local adiabatic quantum search algorithm to speed up the adiabatic process.By studying quantum dynamics of the adiabatic search algorithm with the equivalent two-level system, we derive the transitionless driving Hamiltonian for the local adiabatic quantum search algorithm. We found that when adding a transitionless quantum driving term H_D(t) on the local adiabatic quantum search algorithm, the success rate is 1 exactly with arbitrary evolution time by solving the time-dependent Schr odinger equation in eigen-picture. Moreover, we show the reason for the drastic decrease of the evolution time is that the driving Hamiltonian increases the lowest eigenvalues to a maximum of ON~(1/2).     

一种快速检测圆心的抗噪声亚像素算法

在印刷电路板的自动光学检测中，被检对象的空间对准是一个关键步骤，而传统的图像匹配技术由于其只能在像素级定位，而无法适应印刷电路板精确对准的要求。本文利用圆的几何对称性，提出一种在亚像素精度快速定位圆心的算法。