A Systematic Review of Computer Vision Techniques for Quality Control in End-of-Line Visual Inspection of Antenna Parts

The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.

A path planning method for robot patrol inspection in chemical industrial parks

Safety patrol inspection in chemical industrial parks is a complex multi-objective task with multiple degrees of freedom.Traditional pointer instruments with advantages like high reliability and strong adaptability to harsh environment,are widely applied in such parks.However,they rely on manual readings which have problems like heavy patrol workload,high labor cost,high false positives/negatives and poor timeliness.To address the above problems,this study proposes a path planning method for robot patrol in chemical industrial parks,where a path optimization model based on improved iterated local search and random variable neighborhood descent(ILS-RVND)algorithm is established by integrating the actual requirements of patrol tasks in chemical industrial parks.Further,the effectiveness of the model and algorithm is verified by taking real park data as an example.The results show that compared with GA and ILS-RVND,the improved algorithm reduces quantification cost by about 24%and saves patrol time by about 36%.Apart from shortening the patrol time of robots,optimizing their patrol path and reducing their maintenance loss,the proposed algorithm also avoids the untimely patrol of robots and enhances the safety factor of equipment.

Minimum dose path planning for facility inspection based on the discrete Rao-combined ABC algorithm in radioactive environments with obstacles

Workers who conduct regular facility inspections in radioactive environments will inevitably be affected by radiation.Therefore,it is important to optimize the inspection path to ensure that workers are exposed to the least amount of radiation.This study proposes a discrete Rao-combined artificial bee colony(ABC)algorithm for planning inspection paths with minimum exposure doses in radioactive environments with obstacles.In this algorithm,retaining the framework of the traditional ABC algorithm,we applied the directional solution update rules of Rao algorithms at the employed bee stage and onlooker bee stage to increase the exploitation ability of the algorithm and implement discretion using the swap operator and swap sequence.To increase the randomness of solution generation,the chaos algorithm was used at the initialization stage.The K-opt operation technique was introduced at the scout bee stage to increase the exploration ability of the algorithm.For path planning in an environment with complex structural obstacles,an obstacle detour technique using a recursive algorithm was applied.To evaluate the performance of the proposed algorithm,we performed experimental simulations in three hypothetical environments and compared the results with those of improved particle swarm optimization,chaos particle swarm optimization,improved ant colony optimization,and discrete Rao’s algorithms.The experimental results show the high performance of the proposed discrete Rao-combined ABC algorithm and its obstacle detour capability.

基于K-S检验的Relief特征基因选择方法

文章在分析两种基因数据分析技术的基础上,提出一种基于K-S检验与Relief特征选择算法相结合的基因识别方法。首先采用K-S检验选择出具有一定区分能力的基因,然后利用Relief算法对筛选出来的基因子集进行特征选择,将该方法分别与K-S检验、Relief算法进行对比,以验证该算法的可行性。

Optimal Search for Hidden Targets by Unmanned Aerial Vehicles under Imperfect Inspections

Assume that a target is hidden or lost in one of several possible locations and is to be found by the unmanned aerial vehicle (UAV). A target can be either a hostile object or missing personnel in remote areas. Prior probabilities of target locations are known. Inspection operations done by the UAVs are imperfect, namely, probabilities of overlooking the hidden target and probabilities of false alarms exist for any possible location. The UAV has to sequentially inspect the locations so that to find the target with the minimum loss or damage incurred by the target before it is detected subject to a required level of confidence of target identification. A fast (polynomial-time) priority-based algorithm for finding an optimal search strategy is developed.

Gaussian Distance Weighted Algorithm for Geometric Characteristics of Three-Dimensional Discrete Curves

Discrete curves are composed of a set of ordered discrete points distributed at the intersection of the scanning plane and the surface of the object. In order to accurately calculate the geometric characteristics of any point on the discrete curve, a distance-based Gaussian weighted algorithm is proposed to estimate the geometric characteristics of three-dimensional space discrete curves. According to the definition of discrete derivatives, the algorithm fully considers the relative position difference between a specific point and its neighboring points, introduces the distance weighting idea, and integrates the smoothing strategy. The experiment uses two spatial discrete curves for uniform and non-uniform sampling, and compares them with two commonly used estimation algorithms. The comparative analysis is carried out in terms of sampling density, neighborhood radius and noise resistance. The experimental results show that the Gaussian distance weighted algorithm is effective and provides an efficient algorithm for underground pipeline safety detection.

光储充检放智能电站K-means聚类能效计量检测方法

光伏出力具有较强的不确定性和随机性,降低了光储充检放一体化能效计量检测性能。为此,在随机采样模式下,提出光储充检放智能电站能效计量检测方法。首先分析光储充检放智能电站的拓扑结构;其次采用K-means聚类算法聚类处理光伏出力数据,通过Beta分布根据电站拓扑结构生成光伏出力场景,为能效计量检测提供相关数据;最后构建电站能效检测模型,完成光储充检放智能电站能效的计量检测。实验结果表明,所提方法可精准检测光储充检放智能电站的供电功率和充放电功率,能效检测相对偏差小。

多障碍环境下巡检机器人路径规划优化研究

针对大规模、密集的障碍物分布,高效地搜索最佳路径是一个挑战,为规划出更短的巡检路线,并实现多障碍环境下的灵活避障,文中提出一种多障碍环境下巡检机器人路径规划优化方法。使用二维矩阵构建巡检环境模型,应用D*算法在巡检环境模型中进行巡检机器人路径规划,并将传统D*算法中的扩展步长方式改变为自适应扩展步长,使机器人在面积较大的巡检场地能够更快地完成巡检;将代价函数由欧氏距离替换为切比雪夫诺距离和曼哈顿距离融合的代价函数,并引入了平滑度函数优化线路规划结果,使规划的路径更为平滑,在遇到由于多种原因产生的新障碍物时可以重新规划路径。通过实验结果可知,无论是静态地图还是动态地图,该方法均可以快速准确地规划出一条最佳路线,并且在多种环境中应用该方法能够高效获取路径规划结果。

Toward Real Scenery:A Lightweight Tomato Growth Inspection Algorithm for Leaf Disease Detection and Fruit Counting

The deployment of intelligent surveillance systems to monitor tomato plant growth poses substantial challenges due to the dynamic nature of disease patterns and the complexity of environmental conditions such as background and lighting.In this study,an integrated cascade framework that synergizes detectors and trackers was introduced for the simultaneous identification of tomato leaf diseases and fruit counting.We applied an autonomous robot with smartphone camera to collect images for leaf disease and fruits in greenhouses.

基于k-trajectory算法的电力线巡检机器人的轨迹平滑方法

研究了一种电力线路巡检飞行机器人轨迹平滑的方法。首先,利用坐标变换将机器人在三维空间中的运动映射到二维平面上;其次,在符合飞行机器人动力学规律的基础上,阐述了在k-trajectory算法基础上进行轨迹平滑的方法,分析了三种情况下机器人的飞行轨迹;最后,通过计算机仿真实验证明了该方法的正确性和有效性。

数字孪生技术下电网设备质量检测方法研究

电网设备结构复杂且处于高压电网运行环境中,受光照、高温等因素影响,会导致以人工为基础的电网设备质量检测存在效果不佳、自动性差等问题。为此,提出数字孪生技术下电网设备质量检测方法。采用数字孪生技术建立电网设备虚拟三维模型,实现物理空间到虚拟数据的映射。通过改进粗糙K均值算法对电网设备虚拟三维模型中的电网设备数据聚类,以获取电网设备特征参数。基于电网设备状态质量指数和未来状态质量指数完成电网设备质量自动检测。试验结果表明,所提方法的电网设备质量自动检测精度高、数据聚类时间短,而故障检测精度高达97%。该研究为实现电力数据挖掘提供了技术支持。

电力巡检机器人路径规划方法综述

随着电力系统规模和复杂性的不断增加,传统的人工巡检方式已很难满足当下电力巡检要求。巡检机器人因具有安全性、可靠性和智能性已被逐步应用到电力巡检中。机器人在运行过程中通过提前预测、自主决策或智能导航的方式完成全局遍历与避障。电力巡检的核心任务是规划出一条安全且最优的运行轨迹。系统地分析和梳理了巡检机器人巡检过程中的各类方法;阐述了在变电站和输电线路等不同场景中算法的应用;综述了各类路径规划算法的研究现状和改进方法。在此基础上,对未来电力巡检机器人路径规划的研究和发展方向做出了展望。

迈向智能监察:人工智能赋能国家监察的逻辑与进路

新一代人工智能的兴起为国家治理注入新动能,为国家监察工作的数字化、智能化指明方向。人工智能赋能国家监察具有以“数据全覆盖”实现“监察全覆盖”、从人力监督到智能监督等现实价值。受限于国家监察的初创性与人工智能的局限性,国家监察智能化呈现阶段性特征。国家监察的智能化发展围绕数据、算法、算力三大核心要素,实现监察“块数据”构建、面向国家监察的语言模型嵌入及算力资源的优化供给。针对人工智能存在的数据隐私、数据安全等技术性风险,可从人工智能政府采购的制度化与规范化、建构监察对象数据隐私与安全保护机制和人工智能监察应用的纠错与救济机制等方面予以防范和化解。

基于SLAM技术的矿区巷道巡检机器人路径规划优化

针对矿区巷道环境复杂、道路狭窄等特点,提出了一种基于SLAM(SimultaneousLocalizationandMapping)技术的矿区巷道巡检机器人路径规划优化方法,实现机器人的自主定位和地图构建。采用激光雷达、RGB-D相机等多种传感器相融合,获取矿区三维点云数据,并使用SLAM算法实时构建矿区三维地图。同时,通过配准当前获取的点云数据与已构建的地图,实现机器人在矿区内的自主定位。针对矿山中存在的狭窄、弯曲、分支等复杂环境,提出了一种增量式A~*优化算法用于路径规划。该算法在传统A~*算法的基础上,引入了路径平滑、走廊宽度约束等优化策略,能生成满足矿区复杂环境约束的平滑可行路径。算法采用增量式方式更新,只需对改变的局部区域重新进行路径搜索,大大减少了整体路径规划的计算耗时。通过试验验证该算法性能,结果表明:与传统路线规划方法相比,该算法能够快速、精准地完成巡检任务,为矿区巷道巡检机器人推广应用提供了参考。

基于K-L变换的无人机巡检图像特征提取方法

为了提升特征提取效果,提出了基于K-L变换的无人机巡检图像特征提取方法。利用双边滤波算法去除无人机巡检图像噪声,通过Retinex算法增强去噪后图像亮度,提升图像清晰度。实验证明:该方法可有效去除图像内部噪声,增强图像亮度,提升图像清晰度;可有效提取图像特征,样本维数为3时,特征值与特征向量偏离度最低;特征提取时的真实接受率高至0.9以上。

基于GA-SVM的钢轨廓形类型在线识别算法研究

针对轨道交通日常运维中钢轨廓形自动化检测识别率不高的情况,提出了一种基于几何描述符和支持向量机(SVM)的高精度钢轨廓形在线识别算法。利用结构光传感器对钢轨廓形数据进行采集,采用几何去噪算法对廓形进行离群点剔除和重采样预处理。通过廓形几何描述符对不同类别钢轨廓形进行特征提取,制作廓形特征数据集用于训练SVM。采用遗传算法(GA)对SVM模型参数进行优化选取。将优化训练后的SVM模型用于钢轨廓形检测并和传统廓形识别方法进行对比。结果表明:提出的采用几何描述符的GA-SVM模型平均准确率达到99.62%,单帧廓形识别用时6.43 ms,能有效提升廓形识别准确率与高速性,满足轨道车辆在线检测的需求,并为轨道自动化检测提供了理论和技术支撑。

基于多目标遗传算法的激光雷达养殖车间水质巡检系统

为解决强噪声、低光照、高湿度环境下循环水养殖车间水质巡检难题,该研究提出基于三维激光雷达定位技术的水质巡检系统,同时采用多目标遗传算法求解考虑5个主要因素(包括安全性、距离、平稳性、行程持续时间和无碰撞路径)的机器人最佳巡检路径,并通过试验验证水质巡检系统可行性。研究结果表明,当巡检系统以0.5 m/s巡航时,实际导航路径与目标路径的最大横向偏差为7.2 cm,最大纵向偏差为6.3 cm,最大航向偏差为6.9°;基于多目标遗传算法的路径规划相较于多目标进化和随机同源类查找器方法,在行驶路径相同的条件下路径规划时间分别减少了31%和42%,在同一路径且障碍物数量相同的条件下障碍物碰撞率分别降低了15%和31%。研究结果可为养殖车间水质巡检系统研发提供参考。

基于改进YOLOv5算法的无人机巡检图像智能识别方法

提出一种基于改进YOLOv5算法的无人机巡检图像智能识别方法。该方法构建无人机巡检图像的相邻图像独立坐标系,并利用相对定向法确定图像中共同目标的位置关系。将巡检目标统一转换至同一坐标系下,采用先进的分割技术提取目标纹理特征向量,为后续的图像识别提供了有力支持。在改进YOLOv5算法的过程中,特别注重多尺度网络的选择与融合激活函数及损失函数的优化组合。采用大疆无人机获取建筑裂缝巡检图像进行实验。结果表明,该方法能够在高效率下实现不同类型建筑裂缝的高精度识别,展现出优异的稳定性能。这一研究成果为无人机巡检图像的智能识别提供了新的思路和方法,具有广泛的应用前景和实际价值。

改进CBBA算法的风电场无人机群巡检任务分配

提出一种基于改进的共识捆绑算法(CBBA)的多无人机风电场巡检任务分配方法。首先,根据风电机组基础信息,采用K-均值算法确定无人机数量和安放位置,并构建风电机组巡检价值评估函数,更为合理地评估各风电机组的巡检收益;然后,针对无人机的归巢返程问题,提出一种基于CBBA算法的任务包双向构建策略;最后,为克服传统CBBA算法中存在的因个体“贪婪”导致的整体效益下降问题,设计一种基于类别的奖励制度,以提高任务分配的合理性。试验表明,所提方法可根据风速、风向的变化制定相应的巡检任务分配方案,可为无人机群自主巡检风电场的任务分配提供可参考的方案。

基于改进LSD的HTCC板错位检测算法研究及应用

为解决摄像头模组所用HTCC板脱离固定槽位所导致的HTCC板破裂以及后续零件安装不良等问题,提出一种基于改进LSD的HTCC板错位检测算法。首先应用伽马变换消除产品图像上的阴影;然后对图像进行双边滤波,与原始LSD算法的滤波相比,双边滤波可以保留更完整的边缘信息;提出改进映射关系且应用双线性插值的尺度缩放算法,消除图像的量化伪影,减少图像的失真;提出断线再连接算法,解决原始LSD算法存在的线段过分割问题;最后基于改进的LSD算法设计HTCC板错位检测算法,求得产品中错位的HTCC板的数量和位置。在工厂现场验证得出该方法的检测平均耗时在800 ms以内,检测准确率在97%以上,与原始LSD算法相比,检测耗时至少减少了38%,准确率至少提高了21%,该方法可以满足生产的实时性和准确性要求。