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.

Review of Fabric Defect Detection Based on Computer Vision

In textile inspection field,the fabric defect refers to the destruction of the texture structure on the fabric surface.The technology of computer vision makes it possible to detect defects automatically.Firstly,the overall structure of the fabric defect detection system is introduced and some mature detection systems are studied.Then the fabric detection methods are summarized,including structural methods,statistical methods,frequency domain methods,model methods and deep learning methods.In addition,the evaluation criteria of automatic detection algorithms are discussed and the characteristics of various algorithms are analyzed.Finally,the research status of this field is discussed,and the future development trend is predicted.

Realtime Vision-Based Surface Defect Inspection of Steel Balls

In the proposed system for online inspection of steel balls, a diffuse illumination is developed to enhance defect appearances and produce high quality images. To fully view the entire sphere, a novel unfolding method is put forward based on geometrical analysis, which only requires one-dimensional movement of the balls and a pair of cameras to capture images from different directions. Moreover, a realtime inspection algorithm is customized to improve both accuracy and efficiency. The precision and recall of the sample set were 87.7% and 98%, respectively. The average time cost on image processing and analysis for a steel ball was 47 ms, and the total time cost was less than 200 ms plus the cost of image acquisition and balls' movement. The system can sort 18 000 balls per hour with a spatial resolution higher than 0.01 mm.

Research on automatic inspection system for defects on precise optical surface based on machine vision

In manufacture of precise optical products, it is important to inspect and classify the potential defects existing on the products’ surfaces after precise machining in order to obtain high quality in both functionality and aesthetics. The existing methods for detecting and classifying defects all are low accuracy or efficiency or high cost in inspection process. In this paper, a new inspection system based on machine vision has been introduced, which uses automatic focusing and image mosaic technologies to rapidly acquire distinct surface image, and employs Case-Based Reasoning(CBR)method in defects classification. A modificatory fuzzy similarity algorithm in CBR has been adopted for more quick and robust need of pattern recognition in practice inspection. Experiments show that the system can inspect surface diameter of 500mm in half an hour with resolving power of 0.8μm diameter according to digs or 0.5μm transverse width according to scratches. The proposed inspection principles and methods not only have meet manufacturing requirements of precise optical products, but also have great potential applications in other fields of precise surface inspection.

Rail fastener defect inspection method for multi railways based on machine vision

Purpose–This research aims to improve the performance of rail fastener defect inspection method for multi railways,to effectively ensure the safety of railway operation.Design/methodology/approach–Firstly,a fastener region location method based on online learning strategy was proposed,which can locate fastener regions according to the prior knowledge of track image and template matching method.Online learning strategy is used to update the template library dynamically,so that the method not only can locate fastener regions in the track images of multi railways,but also can automatically collect and annotate fastener samples.Secondly,a fastener defect recognition method based on deep convolutional neural network was proposed.The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region.The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.Findings–Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways.Specifically,fastener location module has achieved an average detection rate of 99.36%,and fastener defect recognition module has achieved an average precision of 96.82%.Originality/value–The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways,which has high reliability and strong adaptability to multi railways.

Backlit Keyboard Inspection Using Machine Vision

A robust system for backlit keyboard inspection is revealed. The backlit keyboard not only has changeable diverse colors but also has the laser marking keys. The keys on the keyboard can be divided into regions of function keys, normal keys, and number keys. However, there might have some types of defects： incorrect illuminating area, non-uniform illumination of specified inspection region（IR）, and incorrect luminance and intensity of individual key. Since the illumination features of backlit keyboard are too complex to inspect for human inspector in the production line, an auto-mated inspection system for the backlit keyboard is proposed in this paper. The system was designed into the operation module and inspection module. A set of image processing methods were developed for these defects inspection. Some experimental results demonstrate the robustness and effectiveness of the proposed system.

Development of an automatic weld surface appearance inspection system using machine vision

In this paper, an automatic inspection system for weld surface appearance using machine vision has been developed to recognize weld surface defects such as porosities, cracks, etc. It can replace conventional manual visual inspection method, which is tedious, time-consuming, subjective, experience-depended, and sometimes biased. The system consists of a CCD camera, a self-designed annular light source, a sensor controller, a frame grabbing card, a computer and so on. After acquiring weld surface appearance images using CCD, the images are preprocessed using median filtering and a series of image enhancement algorithms. Then a dynamic threshold and morphology algorithms are applied to segment defect object. Finally, defect features information is obtained by eight neighborhoods boundary chain code algorithm. Experimental results show that the developed system is capable of inspecting most surface defects such as porosities, cracks with high reliability and accuracy.

A MACHINE VISION SYSTEM FOR INSPECTING WOOD SURFACE DEFECTS BY USING NEURAL NETWORK

With the development of wood industry, the processing of wood products becomemore significant. This paper discusses the developmen of machine vision system used to inspect andclassny the various types of defects of wood suxface. The surface defeds means the variations ofcolour and textUre. The machine vision system is to dated undesirable 'defecs' that can appear onthe surface of rough wood lwnber. A neural network was used within the Blackboard framework fora labeling verification step of the high-level recognition module of vision system. The system hasbere successfully tested on a number of boards from several different species.

基于改进ELM和计算机视觉的核桃缺陷检测

目的:解决现有食品生产企业在核桃缺陷检测中存在的准确性低和效率差等问题。方法:提出一种结合改进极限学习机和计算机视觉的核桃缺陷快速无损检测方法。通过计算机视觉采集核桃大部分表面图像信息,通过高斯滤波对图像进行预处理,通过迭代和保留信息变量法对颜色和纹理特征进行优化,最后,通过改进蝴蝶算法对极限学习机参数(随机权重和偏差)进行优化,实现核桃缺陷快速无损检测,并对所提缺陷检测方法的性能进行验证。结果:试验方法可以实现核桃多种缺陷的有效判别。与常规方法相比,试验方法在核桃缺陷检测中具有更优的检测准确率和效率,检测准确率>98.00%,平均检测时间<9.00 ms。结论:将智能算法和机器视觉技术相结合可以实现核桃缺陷的快速无损检测。

中厚板多层多道焊视觉测量与工艺规划

针对中厚板多层多道焊的焊道测量问题,提出利用焊缝检测和焊道尺寸视觉测量的信息融合自适应微调焊枪位置的方法。首先基于结构光视觉传感器系统采集焊缝图像,在典型图像处理算法的基础上,结合FROSAC提取算法提取焊缝特征信息;将提取到的特征点进行坐标转换,采用视觉测量获得焊道轮廓和尺寸信息,来修正机器人的运动路径;根据焊缝特征信息分析工艺参数对焊道成型的影响,确定焊道层数、各焊道的工艺参数以及焊枪的偏移量,完成多层多道焊接工艺规划;最后基于搭建的机器人焊接视觉系统在12 mm母材上进行V形坡口多层多道焊接试验。结果表明:该方法下坡口填充良好,焊道尺寸平均测量误差小于0.2 mm,满足多层多道焊接工业应用需求。

基于YOLOv7的工件表面缺陷实时检测系统研究

针对现有工件表面缺陷检测方法在准确率、实时性及效率方面的不足,提出一种基于YOLOv7深度学习算法的工件表面缺陷检测系统模型。该模型在保证性能的同时扩大了检测范围,优化了模型结构,解决了作为工件表面缺陷检测主要难点之一的小目标缺陷检测。试验结果表明,与改进前相比,在滚珠丝杠表面缺陷的检测中该模型的精确率得到了明显提高,检测速度和精度均达到实际工业生产效率需求。

基于计算机视觉的工业金属表面缺陷检测综述

针对平面及三维结构金属材料的工业表面缺陷检测,概述了视觉检测技术的基本原理和研究现状,并总结出视觉自动检测系统的关键技术包括光学成像技术、图像预处理技术与缺陷检测器.首先介绍了如何根据检测对象的光学特性选择合适的二维、三维光学成像技术;其次介绍了图像降噪、特征提取、图像分割和拼接等预处理技术的重要作用;然后根据缺陷检测器的实现原理将其分为模板匹配、图像分类、图像语义分割、目标检测和图像异常检测五类,并对其中的经典算法进行了归纳分析.最后,探讨了工业场景下金属表面缺陷检测技术实施中的关键问题,并对该技术的发展趋势进行了展望.

基于改进YOLOv5算法的实木板材表面缺陷检测

实木板材在世界范围内被广泛地应用于建筑、家居、艺术等领域,由于板材表面存在着影响其性能的不同种类的缺陷,而人工去除实木板材缺陷生产效率较低,质量无法保证。为了解决实木板材表面缺陷检测中存在的效率低下及过分依靠工人主观判断的问题,将机器视觉和深度学习方法相结合,利用机器代替人对实木板材进行缺陷检测。具体使用彩色CCD相机采集了赤松和樟子松两种实木板材,裁剪成共计1500张大小为2048×2048像素的木材图片,图片中包含着活节、死节、髓心及裂缝缺陷。在YOLOv5结构基础上,受到了Vision Transformer的启发,在主干网络中使用了全局注意力模块来改进算法,并且针对实木板材的横向锯切方式修改了损失函数,以求在实木板材缺陷检测锯切这一任务中获得更好的效果。充分训练后在测试集上整体mAP达到0.974,召回率达到0.946,较未改进的YOLOv5分别提高了5.98%和9.36%,表现出一定优越性。

面向带钢表面小目标缺陷检测的改进YOLOv7算法

带钢表面小目标缺陷检测是工业质检领域的研究热点。针对热轧带钢表面缺陷检测任务中小目标缺陷易产生漏检的问题,文章提出一种改进的YOLOv7算法。在骨干网络中融入通道空间注意力模块(convolutional block attention module,CBAM)和可重参数化卷积模块,以提升小目标特征的提取效率;采用改进的双向特征金字塔网络(bi-directional feature pyramid network,BiFPN)颈部网络替换原有的路径聚合网络(path aggregation network,PANet)颈部网络,实现对小目标缺陷特征的高效提纯;采用解耦检测头进行检测结果输出,使网络在训练时进一步收敛至更高精度。实验结果表明,改进后的YOLOv7算法在小目标带钢缺陷检测场景下检测精度领先YOLOv7算法4.3 AP50精度,领先YOLOv6算法5.0 AP50精度,领先YOLOX算法4.8 AP50精度,说明该算法可以较好地应用于小目标带钢缺陷检测。

基于深度学习的架空输电线路绝缘子缺陷检测方法研究综述

绝缘子是架空输电线路中不可或缺的部件,对其进行定期检修能确保电力的安全传输和电网的安全运行。人工巡检、机器人巡检、载人直升机巡检、无人机巡检等是现有的输电线路巡检方式。目前,我国电力线路运维的主流模式是“无人机巡检为主,人工巡检为辅”。为了安全起见,操作无人机飞行检修高压输电线路时,必须与线路保持一定的安全距离。由于绝缘子在无人机拍摄的输电线路图像背景复杂多变且状态复杂,小目标种类占比较多。故本文针对架空输电线路绝缘子缺陷检测的场景,分析了目标检测算法的常见类型,并比较了不同算法策略的优缺点,结合实际应用对算法进行改进,最后展望绝缘子缺陷检测的研究趋势。

智慧视觉系统在发动机生产检测上的应用

随着工业4.0时代的到来,生产过程向自动化、智能化、柔性化方向发展,传统依靠人工经验目视检查的方法面临着新的挑战。随着人工智能技术的迅速发展,某发动机工厂实现了工业生产的自动化和智能化,而机器视觉是人工智能中最重要也是最有应用前景的一个方向,它从复杂、多变的视觉信息中提取出有效的信息,并对其进行分析、理解和处理,从而实现对工业产品的自动检测和智能识别。本文通过观察发动机生产过程中的现状,描述出将智能视觉系统应用于发动机的制造检验中,利用图象处理技术来完成发动机关键零件的尺寸和质量的检验。

基于计算机视觉的车载轨道缺陷智能巡检系统设计

为了有效提升车载轨道缺陷智能巡检结果的准确性,提出一种基于计算机视觉的车载轨道缺陷智能巡检系统。在高速运动状态下,采用高分辨率摄像机实时采集和存储车载轨道图像,通过车载轨道图像采集模块、车载轨道缺陷检测和信息管理模块组成系统硬件部分。滤除车载轨道图像中的噪声,提取车载轨道缺陷特征,通过计算机视觉技术建立计算机视觉模型,将特征输入到模型中,最终完成车载轨道缺陷智能巡检。实验结果表明,所提系统可以有效巡检车载轨道缺陷,同时还可以满足实时性检测需求,可为未来铁路的发展提供可靠的价值参考。

基于计算机视觉的电子元件表面缺陷检测算法

为了优化电子元件表面缺陷检测效果,提升缺陷检测率,实现自动、快速、准确的检测目标,引入计算机视觉,提出了一种全新的电子元件表面缺陷检测算法。首先,拍摄电子元件表面图像,对图像进行滤波处理,消除原始图像中多余的噪声;其次,基于阈值分割原理,将电子元件图像划分成若干个区域,从各个区域中提取与元件表面缺陷相关的特征,初步检测电子元件表面缺陷;在此基础上,基于计算机视觉技术,设计电子元件表面缺陷检测算法,进一步精确检测元件表面是否存在缺陷。实验结果表明,提出的检测算法应用后,6种不同类型的电子元件表面缺陷检测率均较高,检测能力更强,准确性和可靠性优势显著。

基于视觉检测技术下机械零件尺寸精度控制系统研究

机械零件产品的加工逐渐呈现出结构复杂、尺寸精度要求高等特点,传统控制技术及方法已经无法满足日益增长的工业生产制造机械零件加工精度要求。该文基于机器视觉检测技术,阐述了基于机器视觉技术下机械零件尺寸精度控制平台的基本组成与工作原理,论述了智能检测算法在该技术中的应用,并通过试验检验其应用效果。结果表明,该技术能够显著提高零件加工精度,同时降低生产成本。研究结果为机械零件尺寸精度控制领域提供了一种先进的解决方案,同时也可以为制造业的智能化发展和提高产品质量水平提供参考和借鉴。

基于机器视觉的冲压开卷线在线板料缺陷检测系统设计

冲压是汽车制造过程的关键环节之一,而开卷线作为冲压的第一道工序,其成品板料缺陷检测对于保证冲压成品件的制造质量具有重要意义。当前主机厂基本采用人工抽样检验形式,存在检测质量不稳定、缺陷不易识别及检测效率低下的问题。随着制造业数智化转型的加速推进,对于冲压开卷线板料缺陷自动检测的需求也日益增长。从机器视觉的定义、分类、测量等方面出发,结合现场实际工况,进行了硬件选型、布局,匹配相应机器视觉软件开发,并采取可靠的测量监控方式,最终成功在冲压开卷线上应用了机器视觉技术,实现了对冲压板料表面缺陷的自动检测和识别。研究成果可为冲压开卷线在线板料缺陷检测系统设计提供借鉴,有助于提高汽车制造质量和生产效率,对于制造业的数智化转型也具有一定的参考价值。