DM Code Key Point Detection Algorithm Based on CenterNet

Data Matrix(DM)codes have been widely used in industrial production.The reading of DM code usually includes positioning and decoding.Accurate positioning is a prerequisite for successful decoding.Traditional image processing methods have poor adaptability to pollution and complex backgrounds.Although deep learning-based methods can automatically extract features,the bounding boxes cannot entirely fit the contour of the code.Further image processing methods are required for precise positioning,which will reduce efficiency.Because of the above problems,a CenterNet-based DM code key point detection network is proposed,which can directly obtain the four key points of the DM code.Compared with the existing methods,the degree of fitness is higher,which is conducive to direct decoding.To further improve the positioning accuracy,an enhanced loss function is designed,including DM code key point heatmap loss,standard DM code projection loss,and polygon Intersection-over-Union(IoU)loss,which is beneficial for the network to learn the spatial geometric characteristics of DM code.The experiment is carried out on the self-made DM code key point detection dataset,including pollution,complex background,small objects,etc.,which uses the Average Precision(AP)of the common object detection metric as the evaluation metric.AP reaches 95.80%,and Frames Per Second(FPS)gets 88.12 on the test set of the proposed dataset,which can achieve real-time performance in practical applications.

Feature detection on point clouds via Gabriel Triangles creation and l1 normal reconstruction

In this paper, we present a robust subneighborhoods selection technique for feature detection on point clouds scattered over a piecewise smooth surface. The proposed method first identifies all potential features using covariance analysis of the local- neighborhoods. To further extract the accurate features from potential features, Gabriel triangles are created in local neighborhoods of each potential feature vertex. These triangles tightly attach to underlying surface and effectively reflect the local geometry struc- ture. Applying a shared nearest neighbor clustering algorithm on ~ 1 reconstructed normals of created triangle set, we classify the lo- cal neighborhoods of the potential feature vertex into multiple subneighborhoods. Each subneighborhood indicates a piecewise smooth surface. The final feature vertex is identified by checking whether it is locating on the intersection of the multiple surfaces. An advantage of this framework is that it is not only robust to noise, but also insensitive to the size of selected neighborhoods. Ex- perimental results on a variety of models are used to illustrate the effectiveness and robustness of our method.

基于LabVIEW的继电保护测试仪自动检测系统设计

针对现有的继电保护测试仪检测程序繁琐,自动化程度低,点对点调试效率低下等问题,提出基于LabVIEW的继电保护测试仪自动检测系统;系统硬件以STM32F407ZGT6为主控芯片研制了继电保护测试仪输出转换装置,自动控制测试仪与标准器连成试验回路,代替人工拆接线行为;系统软件以LabVIEW为开发平台,设计了仪器通信、自动检测、生成报告、强制选择测点等模块,实现了继电保护测试仪的全自动检测;通过对某型继电保护测试仪进行测试,结果表明该系统工作状态稳定,能够实现一键式全自动检测,用户界面友好,可以替代繁琐的人工检测方法,具有一定的工程应用前景。

Multi-Branch High-Dimensional Guided Transformer-Based 3D Human Posture Estimation

The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.

Human steering angle estimation in video based on key point detection and Kalman filter

Human pose recognition and estimation in video is pervasive.However,the process noise and local occlusion bring great challenge to pose recognition.In this paper,we introduce the Kalman filter into pose recognition to reduce noise and solve local occlusion problem.The core of pose recognition in video is the fast detection of key points and the calculation of human steering angles.Thus,we first build a human key point detection model.Frame skipping is performed based on the Hamming distance of the hash value of every two adjacent frames in video.Noise reduction is performed on key point coordinates with the Kalman filter.To calculate the human steering angle,current state information of key points is predicted using the optimal estimation of key points at the previous time.Then human steering angle can be calculated based on current and previous state information.The improved SENet,NLNet and GCNet modules are integrated into key point detection model for improving accuracy.Tests are also given to illustrate the effectiveness of the proposed algorithm.

Abnormal activity detection for surveillance video synopsis

Video synopsis is an effective and innovative way to produce short video abstraction for huge video archives,while keeping the dynamic characteristic of activities in the original video.Abnormal activity,as the critical event,is always the main concern in video surveillance context.However,in traditional video synopsis,all the normal and abnormal activities are condensed together equally,which can make the synopsis video confused and worthless.In addition,the traditional video synopsis methods always neglect redundancy in the content domain.To solve the above-mentioned issues,a novel video synopsis method is proposed based on abnormal activity detection and key observation selection.In the proposed algorithm,activities are classified into normal and abnormal ones based on the sparse reconstruction cost from an atomically learned activity dictionary.And key observation selection using the minimum description length principle is conducted for eliminating content redundancy in normal activity.Experiments conducted in publicly available datasets demonstrate that the proposed approach can effectively generate satisfying synopsis videos.

Discussion on the mechanisms of BO's abdominal acupuncture therapy

This article discussed the mechanisms and precautions of abdominal acupuncture from three different perspectives. First, the relationship between Shénquè （神阙 CV 8） and zang-fu and eight extra meridians stated in the ancient literature explained that the theory of CV 8 as the theoretical core of abdominal acupuncture therapy had literature support and theoretical evidence; second, with emphasis on the diagnosis and treatment based on differentiation of syndromes, four methods for selecting the points, including the ones according to hologram, meridians and collaterals, the Ba-kuo （八 廓） and three energizer, meanwhile, the origin of method selecting the points according to Three Energizer and clinical experience was also addressed; third, some problems about the manipulation of abdominal acupuncture therapy were put forward, such as selection of depth, direction, needle tool, as well as personal ideas and precautions, in order to ensure the clinical effect.

基于小波包的永久性单相接地故障的故障选线的仿真研究

利用小波包的分频特性和重构算法,分别求取各线路中零序电流各频带能量,选出能量最大的频带为特征频带,对该频带的零序电流和系统零序电压通过模极大值和极性进行比较初步选定出可能故障的线路,然后在此基础上利用检测并联电阻投入瞬间在零序电流上所产生的二次奇异点最大值的方法来确定故障线路。仿真结果表明了该方法的有效性和正确性。

基于改进聚类中心分析法的红外行人分割

远红外图像中人体目标分割阈值自动选取算法的鲁棒性较差。为此,从远红外图像的成像机理出发,提出一种改进的K均值聚类中心分析法。当所属类别不同时,聚类前呈线性分布的聚类中心会在聚类后明显转折。根据该特点,将聚类后待测类别的实际聚类中心值与理论聚类中心预测值的绝对差值作为测度函数,选择转折点并确定图像分割的阈值。实验结果表明,该算法具有良好的鲁棒性与抗噪性。

频域扩频码片级差分检测的误码率性能分析

该文提出了一种频域扩频系统中的差分检测方法:频域扩频码片级差分检测。通过利用判决变量的矩生成函数,并采用鞍点近似的分析方法,得到了频域扩频码片级差分检测的误码率性能的解析表达式,同时进行了仿真验证。分析和仿真结果表明,频域扩频码片级差分检测具有抵抗频率选择性衰落的能力,在信道的相干带宽不小于两个码片对应的频带宽度的前提下,随着信道多径时延扩展的增加,其误码率性能有进一步的提高。

常见电阻器的选用要点及检测方法

电阻器是电子电路元件中应用最广泛的一种,在电子设备中约占元件总数的30%以上。文章对不同应用电路中普通固定电阻器、熔断电阻器、特殊电阻器(热敏、压敏、光敏)的选用要点以及检测方法做了简单的介绍。

已知深度范围缺陷的选频带脉冲涡流检测方法

为进一步提高对一定深度范围的局部减薄缺陷的检测能力,提出了选频带脉冲涡流检测方法。介绍了选频带脉冲涡流的工作原理,说明了选频带脉冲涡流激励信号的选取原则,比较了传统方波脉冲涡流与选频带脉冲涡流对一定深度范围的局部减薄缺陷的检测灵敏度,并考察了选频带脉冲涡流的提离交叉点特征。数值实验结果表明:对于一定深度范围的局部减薄缺陷,选频带脉冲涡流比传统方波脉冲涡流检测灵敏度更高,且选频带脉冲涡流提离交叉点的特征能反映出缺陷深度。

基于压电式压力传感器的压力容器气密性检测方法

针对低压条件下压力容器气密性检测中存在的微小泄漏检测易受环境噪声干扰、漏点定位困难、对气体介质具有选择性等问题,提出一种基于压电式压力传感器的压力容器气密性移动式检测方法。利用压电式压力传感器检测压力容器漏孔处产生的气体射流,克服了检测仪器对气体介质的选择性;采用气密性移动检测方式,利用检测用传感器可以接近压力容器器壁外表面的特点和仪表的实时计算能力,克服了环境噪声对检测结果的干扰,同时又提高了气密性检测的灵敏度和漏点定位精度,实现了低压条件下微小泄漏的检测。低压容器气密性检测的测试结果验证了所提方法的可行性和有效性,为压力容器的气密性检测提供了一种新的解决思路。

基于Gabor变换与区域生长的瓷砖表面缺陷视觉检测方法研究

针对瓷砖工业生产中表面缺陷检测严重依赖人工的难题和已有视觉检测技术的不足,提出基于Gabor变换与区域生长的瓷砖表面缺陷检测方法。首先采用中值滤波和局部直方图均衡化实现图像去噪和对比度增强,基于Gabor变换函数在变换域进一步对瓷砖表面图像进行处理,较好地消除光照不均和表面反光影响。然后基于自动区域生长算法实现瓷砖表面缺陷区域准确分割。最后采用双向积分投影实现缺陷区域标注。针对瓷砖表面的裂缝、孔洞、麻面、色差缺陷的实验证明所提方法的有效性和可行性。

基于点特征的单目视觉里程计实现

本文采用特征点检测实现移动机器人的单目视觉里程计,通过对图像进行网格划分保证特征点均匀分布,提高特征点匹配效率。并行计算基础矩阵及单应矩阵实现单目视觉里程计的自动初始化,采用RANSAC算法剔除外点提高特征点匹配质量。设定运动模型及参考帧模型加速帧间运动估计,并通过合理的关键帧选取策略及图优化提高相机位姿精度及鲁棒性。利用TUM数据集测试本文设计的单目视觉里程计,实验轨迹误差为3.58 m,通过Turtlebot2移动机器人对真实环境进行测试,实验表明本文方法的有效性。

基于特征点检测的最佳视点选择方法

针对三维模型视点选择问题,提出了基于特征点检测的最佳视点选择方法。算法首先根据三维网格模型的顶点邻接区域信息计算顶点的局部高度,即顶点的显著度大小;然后基于顶点的局部高度值大小,结合非极大值抑制算法进行三维模型表面特征点检测;最后针对视点球体上的每个候选视点分析该视点下可见特征点的几何分布信息,计算视点质量,从而筛选出最佳视点。实验结果验证了基于特征点检测的三维模型视点选择方法的合理性和优越性,所选择的最佳视点能够提供三维模型较多的几何结构和视觉特征信息。

多状态系统测试建模与测试点选取方法

为解决测试性设计过程中的退化状态测试问题,提出了基于结构和功能的多状态系统测试建模与测试点选取方法。首先,结合多状态系统中对性能状态的描述,明确了以结构和功能为对象的多状态系统测试建模的总体思路,并对模型中的结构、功能和测试相关描述进行了分析;其次,对状态测试问题进行了分析,并对单个测试能够检测的状态集与测试集组合能够检测的状态集的计算方法进行了研究,并提出状态检测率的概念对系统状态的测试指标进行量化;最后,考虑到系统测试性设计与分析中包含故障的测试性问题,结合故障检测率、故障隔离率和状态检测率,提出了测试代价约束下的多状态系统测试点选取方法。结果表明:多状态系统测试建模方法不仅能够对故障的测试性进行分析还能够对退化状态的测试性进行分析;相较于二态系统测试选择方法得到的最佳测试集,在测试代价限制的情况下,多状态系统测试选择方法得到的最佳测试集能够获得较高的状态检测率;在测试代价无限制的情况下,多状态系统测试选择方法得到的最佳测试集不仅能够获得较高的状态检测率,还能获得与二态系统相同的最大故障检测率和故障隔离率。

人脸特征点定位及其在心率检测中的应用研究

在人脸检测基础上,进一步确定眼睛、嘴巴、脸部外形轮廓等位置的问题称为人脸特征点定位,是人脸分析识别的关键技术。通过仪器测量心脏每分钟跳动的次数称为心率检测,是评估心脏功能的重要途径。随着心脏的跳动,血液在人体的循环流动会导致人体肤色发生相应的变化,据此可以进行心率测量。实际中,皮肤区域选择是心率分析测量的关键。总结了人脸特征点定位的主要方法,研究了基于AAM(Active Appearance Models)模型特征点定位的有效皮肤区域选择方法。通过提取信噪比高的人脸区域进行分析,测量得到心率信息。实验结果表明,该方法能有效提高心率检测准确率和鲁棒性。

An Intelligent Framework for Recognizing Social Human-Object Interactions

Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public areas.It involves localizing the human and object targets and then identifying the interactions between them.However,it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient classifiers.Hence,the proposed system offers an automated body-parts-based solution for HOI recognition.This system uses RGB(red,green,blue)images as input and segments the desired parts of the images through a segmentation technique based on the watershed algorithm.Furthermore,a convex hullbased approach for extracting key body parts has also been introduced.After identifying the key body parts,two types of features are extracted.Moreover,the entire feature vector is reduced using a dimensionality reduction technique called t-SNE(t-distributed stochastic neighbor embedding).Finally,a multinomial logistic regression classifier is utilized for identifying class labels.A large publicly available dataset,MPII(Max Planck Institute Informatics)Human Pose,has been used for system evaluation.The results prove the validity of the proposed system as it achieved 87.5%class recognition accuracy.

基于毫米波雷达和摄像头的农用车辆障碍物检测

本文针对单个传感器仅能获得局部环境特征信息,为进一步提高障碍物检测的准确性,提出一种融合毫米波雷达和视觉传感器的障碍物检测方法。通过毫米波雷达得到障碍物的数量与坐标点,同时对摄像头采集到的场景图像进行预处理,得到待分割图像。然后以毫米波雷达得到的坐标相对应的像素坐标为种子点,采用区域生长法进行图像分割。至此,即可检测到障碍物的位置和轮廓信息。