Development of a vision-based lane detection system considering configuration aspects

Vision-based lane-sensing systems require accurate and robust sensing performance in lane detection. Besides, there exists trade-off between the computational burden and processor cost, which should be considered for implementing the systems in passenger cars. In this paper, a stereo vision-based lane detection system considering sensor configuration aspects such as field of view (FOV), span pixels, resolution, etc is developed. An inverse perspective mapping method is formulated based on the relative correspondence between the left and right cameras so that the 3D road geometry can be reconstructed in a robust manner. The selection rule of the sensor configuration and specifications is investigated for a standard highway. Based on the selected sensor configurations, it is shown that sensing region range on the camera image coordinate can be determined for the best lane-sensing performance. The proposed system is implemented on a passenger car and its real-time sensing performance is verified experimentally.     

单目视觉融合激光投射的无人机障碍探测方法

针对微小型无人机在飞行作业任务中的主动避障需求,提出一种用于微小型无人机避障的、基于单目视觉与主动激光点阵投射的障碍探测方法。使用单目相机采集投射的激光点阵图案,经过图像分割、聚类、质心提取等处理过程,通过像面激光线方程约束快速排除特征一致激光点的歧义,使用激光点探测出无人机前方空间中障碍的方位信息。实验验证装置在基线距离为65 mm,工作距离为7 m的条件下,障碍探测的相对误差在1.5%以内。该方法精度高、时间复杂度低,可满足低算力的微小型无人机对障碍探测方法的需求,为进一步避障策略的生成提供数据支撑。     

An Adaptive Deblurring Vehicle Detection Method for High-Speed Moving Drones: Resistance to Shake

Vehicle detection is an essential part of an intelligent traffic system, which is an important research field in drone application. Because unmanned aerial vehicles (UAVs) are rarely configured with stable camera platforms, aerial images are easily blurred. There is a challenge for detectors to accurately locate vehicles in blurred images in the target detection process. To improve the detection performance of blurred images, an end-to-end adaptive vehicle detection algorithm (DCNet) for drones is proposed in this article. First, the clarity evaluation module is used to determine adaptively whether the input image is a blurred image using improved information entropy. An improved GAN called Drone-GAN is proposed to enhance the vehicle features of blurred images. Extensive experiments were performed, the results of which show that the proposed method can detect both blurred and clear images well in poor environments (complex illumination and occlusion). The detector proposed achieves larger gains compared with SOTA detectors. The proposed method can enhance the vehicle feature details in blurred images effectively and improve the detection accuracy of blurred aerial images, which shows good performance with regard to resistance to shake.     

Detecting Unfavorable Driving States in Electroencephalography Based on a PCA Sample Entropy Feature and Multiple Classification Algorithms

Unfavorable driving states can cause a large number of vehicle crashes and are significant factors in leading to traffic accidents. Hence, the aim of this research is to design a robust system to detect unfavorable driving states based on sample entropy feature analysis and multiple classification algorithms. Multi-channel Electroencephalography (EEG) signals are recorded from 16 participants while performing two types of driving tasks. For the purpose of selecting optimal feature sets for classification, principal component analysis (PCA) is adopted for reducing dimensionality of feature sets. Multiple classification algorithms, namely, K nearest neighbor (KNN), decision tree (DT), support vector machine (SVM) and logistic regression (LR) are employed to improve the accuracy of unfavorable driving state detection. We use 10-fold cross-validation to assess the performance of the proposed systems. It is found that the proposed detection system, based on PCA features and the cubic SVM classification algorithm, shows robustness as it obtains the highest accuracy of 97.81%, sensitivity of 96.93%, specificity of 98.73% and precision of 98.75%. Experimental results show that the system we designed can effectively monitor unfavorable driving states.     

Use of nonlinear asymmetrical shock absorber to improve comfort on passenger vehicles

In this study the behaviour of two different types of shock absorbers, symmetrical (linear) and asymmetrical (nonlinear) is compared for use on passenger vehicles. The analyses use different standard road inputs and include variation of the severity parameter, the asymmetry ratio and the velocity of the vehicle. Performance indices and acceleration values are used to assess the efficacy of the asymmetrical systems. The comparisons show that the asymmetrical system, with nonlinear characteristics, tends to have a smoother and more progressive performance, both for vertical and angular movements. The half-car front asymmetrical system was introduced, and the simulation results show that the use of the asymmetrical system only at the front of the vehicle can further diminish the angular oscillations. As lower levels of acceleration are essential for improved ride comfort, the use of asymmetrical systems for vibrations and impact absorption can be a more advantageous choice for passenger vehicles.     

超声波测距导盲车关键技术的研究

为了确保盲人或者视觉受损人群独自安全出行、真正解决其出行不便的困难,设计一款成本低廉、精度高,能够得到广泛推广使用的盲人辅助产品是很有必要的。通过分析提高测距精度和减小盲区的方法,设计了导盲车系统。系统由超声波测距和语音播报两部分组成。通过旋转云台搭载超声波探头检测障碍物距离和方位以及路面凹坑,车底前方的电极检测路面积水;由STM32微处理器对各信号进行处理。采用较高的时钟频率来提高测距精度,增加温度传感器补偿进一步保证精度;安装多个探头以及使用旋转云台,通过旋转扫描以减小盲区,再由文字转语音芯片SYN6288播报路况信息。由于采用旋转扫描检测,经过设计实物并测试,基本上能够覆盖前方约 、5-300cm扇形区域。测距分辨率可达到1cm。     

特征增强SSD算法及其在遥感目标检测中的应用

为了提高对复杂场景下多尺度遥感目标的检测精度,提出了基于多尺度单发射击检测(SSD)的特征增强目标检测算法.首先对SSD的金字塔特征层中的浅层网络设计浅层特征增强模块,以提高浅层网络对小目标物体的特征提取能力;然后设计深层特征融合模块,替换SSD金字塔特征层中的深层网络,提高深层网络的特征提取能力;最后将提取的图像特征与不同纵横比的候选框进行匹配以执行不同尺度遥感图像目标检测与定位.在光学遥感图像数据集上的实验结果表明,该算法能够适应不同背景下的遥感目标检测,有效地提高了复杂场景下的遥感目标的检测精度.此外,在拓展实验中,文中算法对图像中的模糊目标的检测效果也优于SSD.     

基于MSE模型的高分辨率遥感图像变化检测

目前,传统高光谱分辨率遥感图像变化检测方法大多基于单一特征信息进行处理,没有综合影像所有特征信息,难以检测出完整的变化信息。为此,提出了一种基于多特征流形嵌入模型（multiview spectral embedding, MSE）的高分辨遥感图像变化检测方法,利用该模型对图像特征变化信息向量中所有的变化信息进行融合,获得完整的检测结果。实验结果表明,本文方法的检测精度好于传统的变化检测方法,并且稳定性良好。     

Robust Vehicle Speed Measurement Based on Feature Information Fusion for Vehicle Multi-Characteristic Detection

A robust vehicle speed measurement system based on feature information fusion for vehicle multi-characteristic detection is proposed in this paper. A vehicle multi-characteristic dataset is constructed. With this dataset, seven CNN-based modern object detection algorithms are trained for vehicle multi-characteristic detection. The FPN-based YOLOv4 is selected as the best vehicle multi-characteristic detection algorithm, which applies feature information fusion of different scales with both rich high-level semantic information and detailed low-level location information. The YOLOv4 algorithm is improved by combing with the attention mechanism, in which the residual module in YOLOv4 is replaced by the ECA channel attention module with cross channel interaction. An improved ECA-YOLOv4 object detection algorithm based on both feature information fusion and cross channel interaction is proposed, which improves the performance of YOLOv4 for vehicle multi-characteristic detection and reduces the model parameter size and FLOPs as well. A multi-characteristic fused speed measurement system based on license plate, logo, and light is designed accordingly. The system performance is verified by experiments. The experimental results show that the speed measurement error rate of the proposed system meets the requirement of the China national standard GB/T 21555-2007 in which the speed measurement error rate should be less than 6%. The proposed system can efficiently enhance the vehicle speed measurement accuracy and effectively improve the vehicle speed measurement robustness.     

基于中心点的遥感图像多方向舰船目标检测

针对目前基于深度学习的舰船目标斜框检测方法存在计算量大、效率低的问题,提出一种基于目标中心点的单阶段检测模型.由于舰船中心点不受舰船分布方向影响,模型主要思想是以目标中心点检测为基础,回归中心点处目标斜框的尺度和方向.首先设计特征提取网络,将卷积神经网络细节信息丰富的底层特征与语义信息丰富的高层特征融合起来形成特征图;然后将特征图输入到三个检测分支,分别预测目标中心点、中心点偏移值以及斜框的尺度与方向;设计组合损失函数对网络进行训练,并改进非极大值抑制算法以适应目标斜框检测的需要.在公开的SAR图像舰船目标检测数据集与光学遥感图像上进行了实验,实验结果表明,测试集平均准确率达0.906,检测精度与速度均优于其它检测模型,充分验证了所提算法的有效性.     

Fusion of 3D laser scanner and depth images for obstacle recognition in mobile applications

The problem of obstacle detection and recognition or, generally, scene mapping is one of the most investigated problems in computer vision, especially in mobile applications. In this paper a fused optical system using depth information with color images gathered from the Microsoft Kinect sensor and 3D laser range scanner data is proposed for obstacle detection and ground estimation in real-time mobile systems. The algorithm consists of feature extraction in the laser range images, processing of the depth information from the Kinect sensor, fusion of the sensor information, and classification of the data into two separate categories: road and obstacle. Exemplary results are presented and it is shown that fusion of information gathered from different sources increases the effectiveness of the obstacle detection in different scenarios, and it can be used successfully for road surface mapping.     

行驶汽车环境中的话音活动检测研究

话音活动检测是语音交互和通信系统的重要部分,其作用是区分输入信号中的语音段和背景噪声段,检测的依据主要是语音和噪声的各种时频特性,其中,浊语音的周期性和谐波特性是一种广泛应用的特征。但是在行驶的汽车环境中,由于噪声非平稳且信噪比较低,这类特征较难得到可靠的检测。为此,本文根据浊音谐波结构的基本规律,利用时变噪声环境中各频带信噪比不同的特点,提出一种较为鲁棒的谐波快速检测算法。算法以较小的时频块为分析单元,利用一组基频在对数尺度上变化的谐波模板,自适应地搜索谐波结构清晰的部分,并以此检测浊语音信号。实验证明,该算法能够在行驶的汽车环境中达到较可靠的话音/非话音区别效果。     

基于机器视觉的车辆保险盒在线检测研究

摘要: 车辆保险盒作为汽车电控系统中的一个重要的元器件,其质量好坏直接影响汽车的性能,传统的车辆保险盒检测主要依靠人工检测,检测费时费力,针对该问题,提出一种基于视觉的车辆保险盒在线检测方法,分析了产品图像校正到标准模板图像的位置误差,采用SURF(Speeded Up Robust Feature)算法和平面单应性理论将待检产品图像变换到标准模板位置,利用颜色直方图匹配和模板匹配完成保险盒上元件的检测。实验结果证明,该方法检测效率高,稳定可靠,能够满足在线检测的要求,具有一定实用价值。     

基于可视化图形特征的入侵检测方法

入侵检测是保障网络安全的重要措施,网络攻击手段的多样性和隐蔽性不断增强导致入侵检测愈加困难,迫切需要研究新的入侵检测方法。结合可视化技术和k近邻分类算法,提出一种基于图形特征的入侵检测方法。采用信息增益方法对原始特征进行排序选择,并进行雷达图可视化表示,提取雷达图的图形特征构成新的数据集并送入k近邻分类器进行训练和测试。通过KDDCUP99数据集仿真实验表明,该方法不仅能直观显示攻击行为,而且获得较好的攻击检测性能,对DOS攻击的检测率可达97.9%,误报率为1.5%。     

Injection attack detection using machine learning for smart IoT applications

Smart cities are a rapidly growing IoT application. These smart cities mainly rely on wireless sensors to connect their different components (smart devices) together. Smart cities rely on the integration of IoT and 5G technologies, and this has created a demand for a massive IoT network of connected devices. The data traffic coming from indoor wireless networks (e.g., smart homes, smart hospitals, smart factories , or smart school buildings) contributes to over 80% of the total data traffic of the current IoT network. As smart cities and their applications grow, security and privacy challenges have become a major concern for billions of IoT smart devices. One reason for this could be the oversight of handling security issues of IoT devices by their manufacturers, which enables attackers to exploit the vulnerabilities in these devices by performing different types of attacks, e.g., DDoS and injection attacks. Intrusion detection is one way to detect and mitigate the risk of such attacks. In this paper, an intrusion detection method was proposed to detect injection attacks in IoT applications (e.g. smart cities). In this method, two types of feature selection techniques (constant removal and recursive feature elimination) were used and tested by a number of machine learning classifiers (i.e., SVM, Random Forest, and Decision Tree). The T-Test was conducted to evaluate the quality of this proposed feature selection method. Using the public dataset, AWID, the evaluation results showed that the decision tree classifier can be used to detect injection attacks with an accuracy of 99% using only 8 features, which were selected using the proposed feature selection method. Also, the comparison with the most related work showed the advantages of the proposed intrusion detection method.     

一种复杂环境下移动机器人避障新方法

基于传统人工势场法的机器人避障算法虽然计算量小、实时性好,但在陷阱区域或在障碍物前可能产生震荡,导致机器人不能到达目标点,为解决上述缺陷,提出一种带记忆功能的沿边法。通过记录和分析障碍物边缘人工势场法的局部最小点来判断目标点是否被障碍物包围,从而避免机器人来回震荡和围着目标点旋转的发生。该方法步骤分为沿边行为激活和退出条件、局部最小点记录条件以及目标点被障碍物包围的判断准则。通过实验,结果表明该方法解决了复杂环境下机器人的避障问题。该方法用于复杂环境下的机器人避障是可行的、有效的。     

无人机视频运动目标实时检测及跟踪

对无人机拍摄视频中的地面运动目标提出一种实时检测跟踪算法。该算法利用特征点的对应关系将图像对配准,再对配准图像进行变化检测,根据变化和运动信息检测目标并消除虚警,将检测与跟踪相结合,对目标跟踪失效的情况,能重新正确定位目标,从而获取目标的完整运动轨迹。采用无人机拍摄的地面车辆图像的测试结果表明,算法能有效检测跟踪运动目标,并能达到25 f/s以上的实时处理速度。     

Single-Channel FMCW-Radar-Based Multi-Passenger Occupancy Detection Inside Vehicle

In this paper, we provide the results of multi-passenger occupancy detection inside a vehicle obtained using a single-channel frequency-modulated continuous-wave radar. The physiological characteristics of the radar signal are analyzed in a time-frequency spectrum, and features are proposed based on these characteristics for multi-passenger occupancy detection. After clutter removal is applied, the spectral power and Wiener entropy are proposed as features to quantify physiological movements arising from breathing and heartbeat. Using the average means of both the power and Wiener entropy at seats 1 and 2, the feature distributions are expressed, and classification is performed. The multi-passenger occupancy detection performance is evaluated using linear discriminant analysis and maximum likelihood estimation. The results indicate that the proposed power and Wiener entropy are effective features for multi-passenger occupancy detection.     

Hand gesture pointing location detection

Gesture recognition has been implemented in many systems for different purposes. The way it is used can be different from system to system. Few used them as touch objects using sensors while few use them as pointing object where the camera is in front of the hand, but the location is being pointed by human, can be detected only if the camera is behind that person. This paper presents an automatic system to detect the location on the wall where the user is currently pointing by his hand. This is done with the detection of head and both hands. The shoulders and elbows’ locations are identified using geometry analysis to understand the current gesture of the human and finally the direction where user hand is pointing would be detected. The face was detected using Haar classifier and shoulders were estimated using the rectangle method near the head. This method does not work in poor illumination conditions and is made to detect only one person at a time. Also user is supposed to wear half sleeve or sleeveless shirt for the better segmentation.     

Influence of atmospheric refraction on the performance of an outdoor ultrasonic pulse compression system

Nowadays, many ultrasonic sensory systems are being developed to operate outdoors, where they are finding a variety of applications, such as local positioning, vehicle navigation or obstacle detection. To assure the reliable operation of these systems under any meteorological condition, it is necessary to achieve a thorough comprehension of the effects that the different atmospheric phenomena can have on the propagation of these acoustic waves. This paper deals with one of these phenomena, atmospheric refraction, and its influence on the performance of an ultrasonic system whose signals are detected by matched filtering.