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.

Bridge Condition Assessment by Visual Inspection-Croatian Experience

Most decisions relating to bridge maintenance are founded on assessments that are based on visual inspections conducted by specially trained engineers,using procedures and aids defined in the management system.Visual inspection is the main tool for bridge condition assessment,and is therefore of crucial significance for planning periodic maintenance activities.Paper shall present the study aimed at harmonizing bridge assessment activities,which was conducted in early 2012 in company Hrvatske ceste-Croatian National Road Authority.Small RC bridges were chosen for this study,since visual inspection is usually only tool utilized for their assessment.Ratings obtained by inspection were analyzed using methods of mathematical statistics.The results point to weaknesses in the current assessment system,while the study itself constitutes a good basis for further improvement of management aids,manuals and procedures for bridge inspection.

Automated visual inspection of surface defects based on compound moment invariants and support vector machine

The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these difficulties, we develop a machine vision inspection system. We first compare several kinds of methods for feature extraction and classification, and then present a real-time automated visual inspection system for copper strips surface （CSS） defects based on compound moment invariants and support vector machine （SVM）. The proposed method first processes images collected by hardware system, and then extracts feature characteristics based on grayscale characteristics and morphologic characteristics （Hu and Zernike compound moment invariants）. Finally, we use SVM to classify the CSS defects. Furthermore, performance comparisons among SVM, back propagation （BP） and radial basis function （RBF） neural networks have been involved. Experimental results show that the proposed approach achieves an accuracy of 95.8% in detecting CSS defects.

A smart assistance system for cable assembly by combining wearable augmented reality with portable visual inspection

Background Assembly guided by paper documents is the most widespread type used in the process of aircraft cable assembly.This process is very complicated and requires assembly workers with high-level skills.The technologies of wearable Augmented Reality(AR)and portable visual inspection can be exploited to improve the efficiency and the quality of cable assembly.Methods In this study,we propose a smart assistance system for cable assembly that combines wearable AR with portable visual inspection.Specifically,a portable visual device based on binocular vision and deep learning is developed to realize fast detection and recognition of cable brackets that are installed on aircraft airframes.A Convolutional Neural Network(CNN)is then developed to read the texts on cables after images are acquired from the camera of the wearable AR device.An authoring tool that was developed to create and manage the assembly process is proposed to realize visual guidance of the cable assembly process based on a wearable AR device.The system is applied to cable assembly on an aircraft bulkhead prototype.Results The results show that this system can recognize the number,types,and locations of brackets,and can correctly read the text of aircraft cables.The authoring tool can assist users who lack professional programming experience in establishing a process plan,i.e.,assembly outline based on AR for cable assembly.Conclusions The system can provide quick assembly guidance for aircraft cable with texts,images,and a 3 D model.It is beneficial for reducing the dependency on paper documents,labor intensity,and the error rate.

Image Quality Improvement for Underwater Visual Inspections of Nuclear Power Plants

Visual inspection of the key components of nuclear power plants(NPPs)is important for NPP operation and maintenance. However,the underwater environment and existing radiation will lead to image degradation,thus making it difficult to identify surface defects. In this study,a method for improving the quality of underwater images is proposed.By analyzing the degradation characteristics of underwater detection image,the image enhancement technology is used to improve the color richness of the image,and then the improved dark channel prior(DCP)algorithm is used to restore it. By modifying the estimation formula of transmittance and background light,the correction of insufficient brightness in DCP restored image is realized. The proposed method is compared with other state-of-the-art methods. The results show that the proposed method can achieve higher scores and improve the image quality by correcting the color and restoring local details,thus effectively enhancing the reliability of visual inspection of NPPs.

Development of a certified reference material for visual inspection of unsound maize kernels

Maize unsound kernel content is one of the limited items in maize trade, and generally determined by sensory detection. A certified reference material(CRM) for visual inspection of maize unsound kernels was developed according to a national standard of China, GB 1353-2018, and five items, such as insect-damaged kernels, spotted kernels, broken kernels, sprouted kernels and moldy kernels, were included. Unsound maize kernels were collected from fields or prepared in a laboratory, then screened, and ten kernels demonstrating varying levels of unsoundness or damage for each item were embedded in epoxy resin. The CRM showed excellent homogeneity and stability, which was stable for 14 days at the temperature from-20℃ to 45℃ and for at least 18 months at room temperature out of direct sunlight and strong light. Co-laboratory confirmation showed the CRM conformed to the morphological characteristics described in GB 1353-2018. The research filled the gap of unsound maize kernel CRM in maize detection.

A review of applications of visual inspection technology based on image processing in the railway industry

In order to ensure the safety of railway transportation,it is necessary to regularly check for faults and defects in the railway system.Visual inspection technology is conducive to improving the low efficiency,poor economy and inaccurate detection results of traditional detection methods.This paper introduces the research and contribution of various scholars in the field of visual inspection,summarizes the application and development of visual inspection technology in the railway industry,and finally forecasts the future research direction of visual inspection technology.

Imagery Enhancement and Interpretation for Remote Visual Inspection of Aging Civil Infrastructure

In this paper, we describe an image enhancement and interpretation methodology to enhance and recognize surface defects and critical patterns from remote imagery of sewer pipeline inspection. The objective is to provide inspectors and professionals with better tools to allow them to examine the imagery for condition assessment. We present initial results of a collaboration with a robotic company through a case study on computer-assisted processing and interpretation of sewer pipeline inspection imagery. In the mean time, the described enhancement and interpretation methodology can also be applied to sewer pipeline condition assessment in an offline mode, where this methodology can support professionals’ examination of acquired sewer condition imagery.

Defect inspection of indoor components in buildings using deep learning object detection and augmented reality

Visual inspection is commonly adopted for building operation,maintenance,and safety.The durability and defects of components or materials in buildings can be quickly assessed through visual inspection.However,implementations of visual inspection are substantially time-consuming,labor-intensive,and error-prone because useful auxiliary tools that can instantly highlight defects or damage locations from images are not available.Therefore,an advanced building inspection framework is developed and implemented with augmented reality(AR)and real-time damage detection in this study.In this framework,engineers should walk around and film every corner of the building interior to generate the three-dimensional(3D)environment through ARKit.Meanwhile,a trained YOLOv5 model real-time detects defects during this process,even in a large-scale field,and the defect locations indicating the detected defects are then marked in this 3D environment.The defects areas can be measured with centimeter-level accuracy with the light detection and ranging(LiDAR)on devices.All required damage information,including defect positions and sizes,is collected at a time and can be rendered in the 2D and 3D views.Finally,this visual inspection can be efficiently conducted,and the previously generated environment can also be loaded to re-localize existing defect marks for future maintenance and change observation.Moreover,the proposed framework is also implemented and verified by an underground parking lot in a building to detect and quantify surface defects on concrete components.As seen in the results,the conventional building inspection is significantly improved with the aid of the proposed framework in terms of damage localization,damage quantification,and inspection efficiency.

SpyGlass application for duodenoscope working channel inspection: Impact on the microbiological surveillance

BACKGROUND Patient-ready duodenoscopes were designed with an assumed contamination rate of less than 0.4%;however,it has been reported that 5.4%of clinically used duodenoscopes remain contaminated with viable high-concern organisms despite following the manufacturer’s instructions.Visual inspection of working channels has been proposed as a quality control measure for endoscope reprocessing.There are few studies related to this issue.AIM To investigate the types,severity rate,and locations of abnormal visual inspection findings inside patient-ready duodenoscopes and their microbiological significance.METHODS Visual inspections of channels were performed in 19 patient-ready duodenoscopes using the SpyGlass visualization system in two endoscopy units of tertiary care teaching hospitals(Tri-Service General Hospital and National Taiwan University Hospital)in Taiwan.Inspections were recorded and reviewed to evaluate the presence of channel scratches,buckling,stains,debris,and fluids.These findings were used to analyze the relevance of microbiological surveillance.RESULTS Seventy-two abnormal visual inspection findings in the 19 duodenoscopes were found,including scratches(n=10,52.6%),buckling(n=15,78.9%),stains(n=14,73.7%),debris(n=14,73.7%),and fluids(n=6,31.6%).Duodenoscopes>12 mo old had a significantly higher number of abnormal visual inspection findings than those≤12 mo old(46 findings vs 26 findings,P<0.001).Multivariable regression analyses demonstrated that the bending section had a significantly higher risk of being scratched,buckled,and stained,and accumulating debris than the insertion tube.Debris and fluids showed a significant positive correlation with microbiological contamination(P<0.05).There was no significant positive Spearman’s correlation coefficient between negative bacterial cultures and debris,between that and fluids,and the concomitance of debris and fluids.This result demonstrated that the presence of fluid and debris was associated with positive cultures,but not negative cultures.Further multivariate analysis demonstrated that fluids,but not debris,is an independent factor for bacterial culture positivity.CONCLUSION In patient-ready duodenoscopes,scratches,buckling,stains,debris,and fluids inside the working channel are common,which increase the microbiological contamination susceptibility.The SpyGlass visualization system may be recommended to identify suboptimal reprocessing.

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.

An Overview of Seismic Risk Management for Italian Architectural Heritage

The frequent occurrence of seismic events in Italy poses a strategic problem that involves either the culture of preservation of historical heritage or the civil protection action aimed to reduce the risk to people and goods(buildings,bridges,dams,slopes,etc.).Most of the Italian architectural heritage is vulnerable to earthquakes,identifying the vulnerability as the inherent predisposition of the masonry building to suffer damage and collapse during an earthquake.In fact,the structural concept prevailing in these ancient masonry buildings is aimed at ensuring prevalent resistance to vertical gravity loads.Rarely do these ancient masonry structures offer relevant resistance to actions other than vertical ones and then they are intrinsically vulnerable to stresses induced by the earthquakes.One of the main technical activities carried out by the Civil Protection after an earthquake is to assess the damage in the buildings and to evaluate their own usability.Regarding historical buildings,ad-hoc synthetic forms,drafted in agreement between the Italian Civil Protection Department and the Ministry of Cultural Heritage and Activities and Tourism and based on visual inspection,are adopted by qualified technicians.In this paper,such activities are described and discussed along with the Italian Civil Protection System.However,given the complexity of the main technical activities to be performed after an earthquake there is a need for more accurate methods based on Structural Health Monitoring.

Screening for Precancerous Cervical Lesions in Women of Reproductive Age in the Kara Region of Togo in 2022

Introduction: Worldwide, cervical cancer is the 4th most common cancer in women and is a public health problem. The objective of this study was to estimate the prevalence of precancerous cervical lesions and to describe its associated factors among women of reproductive age in the Kara region of Togo. Methods: A cross-sectional study was conducted from March 6 to 14, 2022 in 11 health centers in Kara. Data were collected using a standardized questionnaire and screening for precancerous cervical lesions was performed by visual inspection of the cervix, after application of 5% acetic acid and Lugol’s. Logistic regression analysis was performed to describe factors associated with precancerous lesions. Results: A total of 728 women with a median age of 36, interquartile range [31 - 41] were enrolled. The prevalence of precancerous cervical lesions was 3.9%, 95% confidence interval (95% CI: [2.6 - 5.4]. Factors associated with the presence of precancerous lesions were age at first sexual intercourse ≥ 18 years (adjusted odds ratio (aOR = 3.67;95% CI [1.17 - 18.4]) and being a sex worker (aOR = 8.14;95% CI [1.96 - 27.1]). HIV infection was not associated with the presence of precancerous lesions. Conclusion: The results of this study underscore the importance of intensifying cervical cancer screening efforts in resource-limited countries for better management. These efforts should prioritize vulnerable populations such as female sex workers.

Discussion on Maintenance Cases of Wind Turbine Components

At present,the greenhouse effect is caused by excessive emission of carbon dioxide.As a result the Arctic ice has melted and sea levels have risen.If it continues to deteriorate,it will cause human catastrophe.In order to avoid direct crisis and development,green energy is the only necessary way.Here,wind power plays an important role.Onshore wind power has been developed in Taiwan for more than 15 years.There are 341 onshore wind turbines that have been built so far.The total installed capacity is 678 MW high.Among them,Tai power occupies a total of 169 stations with a total installed capacity of 294 MW.Offshore wind turbines are also under construction.By 2025,the capacity will be 5 to 6 GW.It can be seen that the supply of wind power in the overall power market will become an important area in the future.Therefore,how to improve the availability and capacity factors of wind turbine power generation will become a top priority for owners.Since most of the world’s best wind farms are in the Taiwan Strait,this is a unique feature of Taiwan,although Taiwan lacks traditional fuels,petroleum,coal,natural gas and other resources.If these abundant solar and wind energy resources can be effectively utilized,in addition to reducing carbon emissions and contributing to the world,the development of green energy can also drive the development of the domestic green energy industry,also through the development of green energy to establish domestic operation and maintenance technology for wind turbines.

Chibuike O Chigbu, MD, Series EditorTrends in cervical cancer screening in developing countries

Developing countries suffer the highest burden of cervical cancers but have the lowest resources. Effective cervical cytology screening programme, along with a network of diagnostic and therapeutic colposcopy centres, like developed countries, is almost impossible to be reproduced in developing countries. Visual inspection methods [e.g., Visual inspection with Lugol's iodine(VILI) and Visual Inspection with Acetic Acid(VIA)] which are cheaper, require less expertise and have the advantage of possible treatment in one setting have been shown to be effective alternatives. The sensitivity to detect CIN2+, by VIA and VILI, have been shown to be 80% and 91% respectively, with a specificity rate of 92% and 85% respectively. Screening by human papillomavirus(HPV) testing has high sensitivity(96.4%) but low specificity(94.1%) to detect CIN2+, when compared to Pap Smear(sensitivity, 55.4% and specificity, 96.8%). A single lifetime HPV testing in a large unscreened population has been shown to significantlyreduce cervical cancer incidence and mortality when compared to cervical cytology, VIA or no screening. HPV testing of self-collected vaginal specimens also helps to overcome religious and socio-cultural barriers towards pelvic examination amongst women in developing countries. Current HPV testing methods are expensive, skill/infrastructure demanding and takes time to produce results. A cheaper HPV test, called careH PV?, which is able to provide results within 2.5 h and requires minimal skill/infrastructure to operate, was designed for use in developing countries. One stop screen and treat facilities using VIA or rapid HPV testing, and cryotherapy, can overcome non-compliance to follow-up which is a major issue in developing countries. Cure rates of 81.4% for CIN1, 71.4% for CIN2 and 68.0% for CIN3 at 6 mo after treatment have been reported. Incorporating telemedicine with cervicography of VIA or VILI or even telecolposcopy, has great potential in cervical cancer screening, especially in countries with vast geographical areas.

Assembly Auxiliary System for Narrow Cabins of Spacecraft

Due to the narrow space and complex structure of spacecraft cabin, the existing asssembly systems can not well suit for the assembly process of cabin products. This paper aims to introduce an assembly auxiliary system for cabin products. A hierarchical-classification method is proposed to re-adjust the initial assembly relationship of cabin into a new hierarchical structure for efficient assembly planning. An improved ant colony algorithm based on three assembly principles is established for searching a optimizational assembly sequence of cabin parts. A mixed reality assembly environment is constructed with enhanced inforamtion to promote interaction efficiency of assembly training and guidance. Based on the machine vision technology, the inspection of left redundant objects and measurement of parts distance in inner cabin are efficiently performed. The proposed system has been applied to the assembly work of a spacecraft cabin with 107 parts, which includes cabin assembly planning, assembly training and assembly quality inspection. The application result indicates that the proposed system can be an effective assistant tool to cabin assembly works and provide an intuitive and real assembly experience for workers. This paper presents an assembly auxiliary system for spacecraft cabin products, which can provide technical support to the spacecraft cabin assembly industry.

Methodology for Road Defect Detection and Administration Based on Mobile Mapping Data

A detailed inspection of roads requires highly detailed spatial data with sufficient precision to deliver an accurate geometry and to describe road defects visually.This paper presents a novel method for the detection of road defects.The input data for road defect detection included point clouds and orthomosaics gathered by mobile mapping technology.The defects were categorized in three major groups with the following geometric primitives:points,lines and polygons.The method suggests the detection of point objects from matched point clouds,panoramic images and ortho photos.Defects were mapped as point,line or polygon geometries,directly derived from orthomosaics and panoramic images.Besides the geometric position of road defects,all objects were assigned to a variety of attributes:defect type,surface material,center-of-gravity,area,length,corresponding image of the defect and degree of damage.A spatial dataset comprising defect values with a matching data type was created to perform the attribute analysis quickly and correctly.The final product is a spatial vector data set,consisting of points,lines and polygons,which contains attributes with further information and geometry.This paper demonstrates that mobile mapping suits a large-scale feature extraction of road infrastructure defects.By its simplicity and flexibility,the presented methodology allows it to be easily adapted to extract further feature types with their attributes.This makes the proposed approach a vital tool for data extraction settings with multiple mobile mapping data analysts,e.g.,offline crowdsourcing.

Posture Adjustment of Microphone Based on Image Recognition in Automatic Welding System

As the requirements of production process is getting higher and higher with the reduction of volume,microphone production automation become an urgent need to improve the production efficiency.The most important part is studied and a precise algorithm of calculating the deviation angle of four types microphones is proposed,based on the feature extraction and visual detection.Pretreatment is performed to achieve the real-time microphone image.Canny edge detection and typical feature extraction are used to distinguish the four types of microphones,categorizing them as type M1 and type M2.And Hough transformation is used to extract the image features of microphone.Therefore,the deviation angle between the posture of microphone and the ideal posture in 2Dplane can be achieved.Depending on the angle,the system drives the motor to adjust posture of the microphone.The final purpose is to realize the high efficiency welding of four different types of microphones.

Build Gaussian Distribution Under Deep Features for Anomaly Detection and Localization

Anomaly detection in images has attracted a lot of attention in the field of computer vision.It aims at identifying images that deviate from the norm and segmenting the defect within images.However,anomalous samples are difficult to collect comprehensively,and labeled data is costly to obtain in many practical scenarios.We proposes a simple framework for unsupervised anomaly detection.Specifically,the proposed method directly employs CNN pre-trained on ImageNet to extract deep features from normal images and reduce dimensionality based on Principal Components Analysis(PCA),then build the distribution of normal features via the multivariate Gaussian(MVG),and determine whether the test image is an abnormal image according to Mahalanobis distance.We further investigate which features are most effective in detecting anomalies.Extensive experiments on the MVTec anomaly detection dataset show that the proposed method achieves 98.6%AUROC in image-level anomaly detection and outperforms previous methods by a large margin.

A mask R-CNN based method for inspecting cable brackets in aircraft

In the aviation industry,cable bracket is one of the most common parts.The traditional assembly state inspection method of cable bracket is to manually compare by viewing 3 D models.The purpose of this paper is to address the problem of inefficiency of traditional inspection method.In order to solve the problem that machine learning algorithm requires large dataset and manually labeling of dataset is a laborious and time-consuming task,a simulation platform is developed to automatically generate synthetic realistic brackets images with pixel-level annotations based on 3 D digital mock-up.In order to obtain accurate shapes of brackets from 2 D image,a brackets recognizer based on Mask R-CNN is trained.In addition,a semi-automatic cable bracket inspection method is proposed.With this method,the inspector can easily obtain the inspection result only by taking a picture with a portable device,such as augmented reality(AR)glasses.The inspection task will be automatically executed via bracket recognition and matching.The experimental result shows that the proposed method for automatically labeling dataset is valid and the proposed cable bracket inspection method can effectively inspect cable bracket in the aircraft.Finally,a prototype system based on client-server framework has been developed for validation purpose.