Subpixel accuracy for extracting groove center based on corner detection

Subpixel accuracy for V-groove center in robot welding is researched and a software measure to increase the accuracy of seam tracking by laser is presented. LOG （ Laplacian of Gaussian ） operator is adopted to detect image edge. Vgroove center is extracted by corner detection of extremum curvature. Subpixel position is obtained by Lagarange polynomial interpolation algorithm. Experiment results show that the method is brief and applied, and is sufficient for the real time of robot welding by laser sensors.

Border Sensitive Knowledge Distillation for Rice Panicle Detection in UAV Images

Research on panicle detection is one of the most important aspects of paddy phenotypic analysis.A phenotyping method that uses unmanned aerial vehicles can be an excellent alternative to field-based methods.Nevertheless,it entails many other challenges,including different illuminations,panicle sizes,shape distortions,partial occlusions,and complex backgrounds.Object detection algorithms are directly affected by these factors.This work proposes a model for detecting panicles called Border Sensitive Knowledge Distillation(BSKD).It is designed to prioritize the preservation of knowledge in border areas through the use of feature distillation.Our feature-based knowledge distillation method allows us to compress the model without sacrificing its effectiveness.An imitation mask is used to distinguish panicle-related foreground features from irrelevant background features.A significant improvement in Unmanned Aerial Vehicle(UAV)images is achieved when students imitate the teacher’s features.On the UAV rice imagery dataset,the proposed BSKD model shows superior performance with 76.3%mAP,88.3%precision,90.1%recall and 92.6%F1 score.

Advanced Border Intrusion Detection and Surveillance Using Wireless Sensor Network Technology

Wireless Sensor Network (WSN) has been emerging in the last decade as a powerful tool for connecting physical and digital world. WSN has been used in many applications such habitat monitoring, building monitoring, smart grid and pipeline monitoring. In addition, few researchers have been experimenting with WSN in many mission-critical applications such as military applications. This paper surveys the literature for experimenting work done in border surveillance and intrusion detection using the technology of WSN. The potential benefits of using WSN in border surveillance are huge;however, up to our knowledge very few attempts of solving many critical issues about this application could be found in the literature.

Efficient People Detection with Infrared Images

This work focuses on the problem of monitoring the coastline, which in Portugal’s case means monitoring 3007 kilometers, including 1793 maritime borders with the Atlantic Ocean to the south and west. The human burden on the coast becomes a problem, both because erosion makes the cliffs unstable and because pollution increases, making the fragile dune ecosystem difficult to preserve. It is becoming necessary to increase the control of access to beaches, even if it is not a popular measure for internal and external tourism. The methodology described can also be used to monitor maritime borders. The use of images acquired in the infrared range guarantees active surveillance both day and night, the main objective being to mimic the infrared cameras already installed in some critical areas along the coastline. Using a series of infrared photographs taken at low angles with a modified camera and appropriate filter, a recent deep learning algorithm with the right training can simultaneously detect and count whole people at close range and people almost completely submerged in the water, including partially visible targets, achieving a performance with F1 score of 0.945, with 97% of targets correctly identified. This implementation is possible with ordinary laptop computers and could contribute to more frequent and more extensive coverage in beach/border surveillance, using infrared cameras at regular intervals. It can be partially automated to send alerts to the authorities and/or the nearest lifeguards, thus increasing monitoring without relying on human resources.

Forest Cover Monitoring and Change Detection in Nfifikh Forest (Morocco)

Peri-urban forests are subject to different dynamics due to several factors. Nfifikh forest is a man-made space, located in suburban of Mohammedia City, belonging to Casablanca, Settat Region, and geographically between Casablanca, the economic and business Capital of Morocco and Rabat, the national political capital. Over the past three decades, it has experienced several significant degradations. The aim of this study is to evaluate and quantify the deforestation within the study area using a forest cover change detection of various vegetation indices and subpixel classification to pick out high density plots with Landsat images TM, ETM+ and OLI. Remote sensing is used to highlight the changes caused through Space-Time. This monitoring might help managers to generate forest management plans and to moderate the speed of deforestation and degradation. The results show a significant change in vegetation cover detected between 1987 and 2015. The Density increased in 2001 while it decreased considerably in 2015.

基于改进Border-SMOTE的不平衡数据工业控制系统入侵检测

工业环境中正常与异常样本间的不平衡特点导致入侵检测模型在进行分类时对少数异常样本识别率较低。然而,工控入侵检测模型尤其注重对异常样本的检测成功率,因此文章引入具有自适应思想的边界SMOTE算法,在边界区域根据样本分布情况合理生成少数样本以降低样本的不平衡性。UCI不平衡数据集上的结果证明了该算法的有效性。然后改进边界SMOTE对原始不平衡工控入侵检测数据集SWaT进行数据预处理,在合成合理攻击数据后使用孪生支持向量机(TWSVM)作为分类器构建入侵检测模型。实验结果表明,该方法提高了对攻击样本的识别能力。

Efficient Anti-Glare Ceramic Decals Defect Detection by Incorporating Homomorphic Filtering

Nowadays the computer vision technique has widely found applicationsin industrial manufacturing process to improve their efficiency. However, it ishardly applied in the field of daily ceramic detection due to the following twokey reasons: (1) Low detection accuracy as a result of ceramic glare, and (2) Lackof efficient detection algorithms. To tackle these problems, a homomorphic filtering based anti-glare ceramic decals defect detection technique is proposed in thispaper. Considering that smooth ceramic surface usually causes glare effects andleads to low detection results, in our approach, the ceramic samples are takenin low light environment and their luminance and details restored by a homomorphic filtering based image enhancement technique. With relatively high quality preprocessed images, an effective ceramic decal defect detection algorithm isthen designed to rapidly locate those out-of-bounds defects and further estimatetheir size. The experimental results show that the proposed scheme could achieveits desired performance.

Automated Contour Detection for Intravascular Ultrasound Image Sequences Based on Fast Active Contour Algorithm

Intravascular ultrasound can provide high-resolution real-time cross-sectional images about lumen, plaque and tissue. Traditionally, the luminal border and medial-adventitial border are traced manually. This process is extremely time-consuming and the subjective difference would be large. In this paper, a new automated contour detection method is introduced based on fast active contour model. Experimental results found that lumen and vessel area measurements after automated detection showed good agreement with manual tracings with high correlation coefficients (0.94 and 0.95, respectively) and small system difference (-0.32 and 0.56, respectively). So it can be a reliable and accurate diagnostic tool.

基于移动设备机器视觉的无砟轨道钢轨爬行状态检测方法

无缝线路钢轨爬行是由于列车的启制动以及外部温度变化导致的钢轨沿纵向发生位移,准确检测钢轨的位移状态变化,在铁路运营安全管理中是一项重要的任务。传统的人工测量方法受限于时间、空间和精度约束,检测效果不够准确。从实际运维环境出发,提出一种基于车载式机器视觉的无砟轨道钢轨纵向位移检测方法。研究包括:在钢轨与轨道板上粘贴位移标志物,使用实验室自研探伤小车搭载线激光扫描仪获取高精度图像信息;提出一种基于改进Yolov8-Pose的关键点检测方法,对钢轨及轨道板上的位移标志物关键点进行识别并确定感兴趣区域;基于Harris亚像素检测方法对标志物的亚像素角点进行定位,进一步提高检测精度;在无砟轨道试验段进行测试,对钢轨纵向位移进行高精度检测。实验结果表明:钢轨纵向位移检测算法在不同实际位移下的均方根误差平均值为0.202 mm,最大值不超过0.5 mm,符合无砟轨道的检测要求。该方法可以在线路上多点连续进行钢轨纵向位移检测,提高了铁路运维效率,确保铁路行车的安全平稳。

基于改进Canny和灰度矩的水下图像边缘检测方法研究

针对水下图像对比度差、模糊,致使检测出的边缘存在不连续和伪边缘的问题,提出一种基于改进的Canny和亚像素的水下图像边缘检测方法。采用改进的Canny算法检测水下图像的像素级边缘,在此基础上采用灰度矩提取图像的亚像素边缘特征,提高边缘的定位精度和检测率。实验结果表明,提出的算法相较于传统的像素级边缘检测算法在边缘轮廓的提取、减少伪边缘、提升边缘精度方面有较大优势,尤其是对深海环境中光源单一以及图像对比度差的物体边缘的检测具有更显著的效果和定位精度。

2023年云南省边境地区牛猪口蹄疫血清学调查

为了解云南省边境地区牛猪口蹄疫(FMD)控制情况,2023年在跨境动物流动频繁的8个边境县市设置监测点,采集牛血清样品1630份、猪血清样品830份,分别进行了O、A型口蹄疫病毒(FMDV)免疫抗体检测,并对检测结果进行了统计分析。结果显示:牛群O、A型FMDV抗体阳性率分别为71.17%、80.12%,猪群分别为31.33%、26.27%;统计分析发现,牛规模场O、A型FMDV抗体阳性率均低于散养户,但无统计学差异(P>0.05);入境牛O、A型FMDV抗体阳性率分别为70.29%、89.14%,本地牛分别为71.37%、77.98%,入境牛的A型抗体阳性率显著高于本地牛(P<0.05)。结果说明:牛群FMD免疫水平达到国家要求,但猪群免疫水平较低。建议云南省边境地区持续开展FMD血清学监测,着重提高牛规模场和猪群的FMD免疫密度和强度,加大走私牛查处力度,严防FMDV传入和扩散。

小模数直齿轮免参数并行快速测量

小模数齿轮在实际制造中,单条产线多台设备通常同时生产多种不同参数的齿轮,迄今无法用单台齿轮测量仪器在生产线上同时实时全检这些不同参数的齿轮。开发了基于Facet的齿轮亚像素边缘定位算法、基于迭代重加权最小二乘的齿轮中心定位法、圆与齿廓交点快速定位法、正弦函数拟合齿数法和齿廓近似测压力角法等;若结合适当的送料与定位机构,本文方法能够在未知参数时,实现多种不同型号齿轮的混合测量,无需装夹,测量效率高。可以实现齿数、模数、压力角、齿顶圆直径、齿根圆直径、全齿高、齿宽、公法线变动量、变位系数、齿廓偏差和形位误差的测量。实验结果表明:该方法测量0.5~1.0 mm模数的直齿轮的重复性精度为4μm。该方法在小模数齿轮柔性化生产线具有重要应用前景。

基于灰度自适应增强的DKDP晶体损伤检测

在磷酸二氢钾(KDP)及磷酸二氘钾(DKDP)晶体损伤研究中需要对图像中损伤点进行统计,为了解决损伤图像中弱小损伤点的低对比度导致统计不准确的问题,提出一种基于局部直方图灰度自适应增强的损伤点检测方法。首先利用图像差分去除高频背景,然后根据图像中每个像素邻域的灰度与方差和整幅图像的灰度与方差之间的差异性,筛选出待增强像素,并通过邻域最大灰度值与全局最大灰度值得出灰度调节系数,对图像进行自适应增强,最后对增强后的图像进行阈值分割及目标分离。实验结果表明该增强算法可以使弱小损伤点的信噪比得到提升,更利于检测出图像中的弱小损伤点,提高DKDP晶体损伤研究中损伤点统计的准确性。

视觉感知模型下模糊图像亚像素边缘检测算法

图像边缘中存在大量图像信息,在图像边缘检测中像素级已经无法完全满足图像信息提取需求,往往需要将像素单位提升到亚像素级别,于是提出一种视觉感知模型下模糊图像亚像素边缘检测算法。构建模糊图像还原模型,采用蛙跳算法优化模糊图像,通过最大似然估计原始图像,生成还原后图像。基于突触传递模型,结合视觉感知模型构造视觉感知神经元模型,利用上述模型对图像边缘像素级检测,再引入B样条插值法获取插入点,对图像像素级边缘插值处理,得到图像亚像素级边缘,实现模糊图像亚像素边缘检测。实验结果表明,所提方法FOM值较高,能够获取更加全面的图像边缘信息,检测到的图像边缘更加清晰,细节更加丰富,并且检测时长较短,可广泛应用于图像检测领域。

融合自适应感受野的目标检测算法

针对不同尺度的目标容易造成模型的特征提取和尺度变化的适应性问题,提出一种融合自适应感受野的目标检测算法。通过对特征图采用拆分、卷积和融合的方式构造通道自适应感受野模块,以提取不同尺度的感受野,提高对尺度变化的适应能力;在网络结构中引入通道自适应感受野模块和RepVGG模块,采用FC模块(concatenate with convolutions)过滤冗余特征,强化模型的特征提取能力;采用Alpha-CIOU损失和知识蒸馏优化训练,提高算法的检测能力。在Pascal VOC和MS COCO数据集上的实验结果表明,该算法在尺度变化、精度和速度等方面取得了优秀的性能。

基于机器视觉的碳毡装配检测算法系统设计

针对碳毡装配过程中碳毡占位、边缘毛刺复杂,人工检测效率低、成本高、接触产生不良影响等问题,设计了一种基于机器视觉的碳毡装配检测算法系统。使用Python语言调用相机获取图像并进行预处理。随后,对图像应用改进的Sobel算子进行初步的边缘检测,获取边缘的粗略坐标信息。接着,采用改进的插值法亚像素边缘检测算法对这些粗略坐标进行细化,得到更精确的坐标信息。然后,利用最小二乘法对这些精确坐标进行直线拟合,得到图像中的四个角点。最终,通过对这些角点的位置关系进行判断和分析。实验结果表明,本系统可以稳定进行碳毡装配检测,具有一定的鲁棒性。

基于深度森林的BGP异常检测方法

一直以来,边界网关协议(Border Gateway Protocol,BGP)异常事件严重影响着互联网的稳定与安全,因此BGP异常检测算法的研究显得尤为重要。针对已应用于BGP异常检测的机器学习算法准确率不高且实验数据集异常种类单一的问题,为了提高准确率并提高方法普适性,引入基于深度森林的异常分类算法。实验采用多个异常事件数据集,根据皮尔森相关系数来剔除冗余无关特征,用于对BGP异常分类,分别采用深度森林算法和其他机器学习算法对数据分类。实验结果表明,深度森林的性能是优于其他算法的。

面向无人边防的复杂环境遮挡小目标检测算法

面对边境复杂环境导致的人工巡检难题和安全风险,部署无人化监控系统对我国边防至关重要。由于摄像头与入侵目标间距离差异导致所拍图像尺度变化显著问题,以及监控目标采用遮挡策略,现有无人边防系统面临误检、漏检率高和实时性不足的挑战。针对该问题,提出了一种平均精度更高、参数量更少、普适性更强的FDB-YOLOv5遮挡小目标检测算法。首先,采集大量不同遮挡面积的人员样本构建数据集;其次,设计了Faster_C3新结构,以减少遮挡小目标检测网络的延迟和参数量,提高模型检测速率;此外,在颈部网络中引入基于点采样的Dysample上采样器,获得更多的局部细节和语义信息,增强模型对小目标的检测能力,同时降低计算开销;最后,使用基于多尺度特征提取BSPPF的空间金字塔池化方法,有效地解决尺度不变性及遮挡目标特征信息损失问题,从而更好地捕获关键信息,提高模型对遮挡小目标检测的稳定性以及鲁棒性。实验结果表明,与基线YOLOv5相比,FDB-YOLOv5的mAP@0.5%达到了91.5%;参数量和计算量分别减少了19.07%与18.40%;模型的检测速度提高了8.83%;与Faster R-CNN、SSD、YOLOv5s、YOLOv8相比,FDB-YOLOv5表现出更优越的性能,可以为边境无人化目标检测技术提供参考。

基于改进YOLOv5的室内楼梯检测方法研究

移动机器人视觉SLAM的楼梯建图过程需要对楼梯特征进行检测识别,传统的边缘检测、直线提取等楼梯检测技术往往视角较为理想、背景较为简单,无法实现栏杆遮挡、复杂背景下的楼梯特征提取;为了解决以上问题,提出了一种可用于移动机器人的改进YOLOv5的楼梯目标检测方法,在输入端引入FenceMask数据增强策略,增加对遮挡楼梯的训练样本数量;通道注意力模块CAM与空间注意力模块SAM采用并行连接的方式组成注意力模块CBAM,加强在复杂环境下对楼梯的特征提取能力;在预测端将NMS与WBF结合,将NMS筛选之后置信度较高且位置相邻的边框进行融合为新的边框,在满足精度要求的情况下改善了Faster-RCNN与SSD检测算法存在的单段多阶楼梯检测速度问题;仿真表明改进的YOLOv5s可以在模型大小18.4 MB的情况下达到82.9%的平均精度,改进的YOLOv5m在增大模型为45.5 MB的情况下平均精度提高为86.5%,均可有效识别栏杆遮挡、复杂背景以及单段长阶梯。

基于误差效应的亚像素边缘检测算法

针对传统亚像素边缘检测算法在实时性、精确性和鲁棒性之间难以平衡的问题,提出了一种基于误差效应的亚像素边缘检测方法。该方法首先建立边缘函数模型,并计算每个像素边缘对应函数方程常数项的近似值和对应误差,然后结合最小二乘法使整体函数模型误差最小,从而获得较为精确的边缘参数。基于标准图像和实际图像的实验测试结果表明,该算法具有快速、鲁棒性强和检测精度高等优点,能够满足实时检测的要求,评价指标优于其他传统算法。