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 functionali...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.展开更多
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...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.展开更多
The textile industry is one of the most important industries in Sri Lanka.In most of the textile garment factories the defects of the fabrics are detected manually.The manual textile quality control usually depends on...The textile industry is one of the most important industries in Sri Lanka.In most of the textile garment factories the defects of the fabrics are detected manually.The manual textile quality control usually depends on eye inspection.Famously,human visual assessment is drawn-out,tiring,and an exhausting errand,including perception,consideration and experience to recognize the fault occurrence.The precision of human visual assessment declines with dull positions and vast schedules.Some of the time slow,costly,and sporadic review is the outcome.In this manner,the programmed automatic visual review safeguards both the fabric quality inspector and the quality.This examination has exhibited that Textile Defect Recognition System is fit for distinguishing fabrics’imperfections with endorsed exactness with viability.With some products 100%inspection is important to ensure the stipulated quality or standard.The classifications for the automated fabric inspection approaches are expanding as the work is vast and complex.According to the algorithm used,the texture analysis problem is classified into different approaches.They are Structural,spectral,model-based methods,Unfortunately,the optimal plan does not yet exist for these vast numbers of applied methods,as each of them has some advantages and disadvantages.展开更多
The manual visual inspections of façade building defects are posing a high and increasing cost for building asset managers,particularly when inspections delay projects or require asset outages,visits to decommiss...The manual visual inspections of façade building defects are posing a high and increasing cost for building asset managers,particularly when inspections delay projects or require asset outages,visits to decommissioned sites or work within hazardous environments.This paper reports on the development,testing and delivery of a working mobile app prototype to facilitate the inspections and documentation of building facade condition monitoring.The work presented builds upon the development of an online platform for remote building inspection based on the integration of methodologies and tools,including VR(virtual reality),and digital photogrammetry to collect real-time data that support automated decision making.The mobile app:(i)allows the user to import 3D models and 2D building plans;(ii)provides the means of first-person exploration of models via a VR headset;and(iii)captures,records and catalogues images of façade defect types,and the date and time.An inspection case study was used to demonstrate and evaluate the mobile app prototype.The Building Inspector app allows building professionals to manage inspections and to track past and ongoing monitoring of the condition of building façades.展开更多
Defect inspection of specular curved surface is a challenging job. Taking steel balls for example, a new method based on reflected pattern integrity recognition is put forward. The specular steel ball surfac...Defect inspection of specular curved surface is a challenging job. Taking steel balls for example, a new method based on reflected pattern integrity recognition is put forward. The specular steel ball surface will totally reflect the patterns when it is placed inside a dome-shaped light source, whose inner wall is modified by patterns with certain regular. Distortion or intermittence of reflected pattern will occur at the defective part, which indicates the pattern has lost its integrity. Based on the integrity analysis of reflected pattern images？ surface defects can be revealed. In this paper, a set of concentric circles are used as the pattern and an image processing algorithm is customized to extract the surface defects. Results show that the proposed method is effective for the specular curved surface defect inspection展开更多
Based on inspection data,the authors analyze and summarize the main types and distribution characteristics of tunnel structural defects.These defects are classified into three types:surface defects,internal defects,an...Based on inspection data,the authors analyze and summarize the main types and distribution characteristics of tunnel structural defects.These defects are classified into three types:surface defects,internal defects,and defects behind the structure.To address the need for rapid detection of different defect types,the current state of rapid detection technologies and equipment,both domestically and internationally,is systematically reviewed.The research reveals that surface defect detection technologies and equipment have developed rapidly in recent years.Notably,the integration of machine vision and laser scanning technologies have significantly improved detection efficiency and accuracy,achieving crack detection precision of up to 0.1 mm.However,the non-contact rapid detection of internal and behind-the-structure defects remains constrained by hardware limitations,with traditional detection remaining dominant.Nevertheless,phased array radar,ultrasonic,and acoustic vibration detection technologies have become research hotspots in recent years,offering promising directions for detecting these challenging defect types.Additionally,the application of multisensor fusion technology in rapid detection equipment has further enhanced detection capabilities.Devices such as cameras,3D laser scanners,infrared thermal imagers,and radar demonstrate significant advantages in rapid detection.Future research in tunnel inspection should prioritize breakthroughs in rapid detection technologies for internal and behind-the-structure defects.Efforts should also focus on developing multifunctional integrated detection vehicles that can simultaneously inspect both surface and internal structures.Furthermore,progress in fully automated,intelligent systems with precise defect identification and real-time reporting will be essential to significantly improve the efficiency and accuracy of tunnel inspection.展开更多
Skin defect inspection is one of the most significant tasks in the conventional process of aircraft inspection.This paper proposes a vision-based method of pixel-level defect detection,which is based on the Mask Scori...Skin defect inspection is one of the most significant tasks in the conventional process of aircraft inspection.This paper proposes a vision-based method of pixel-level defect detection,which is based on the Mask Scoring R-CNN.First,an attention mechanism and a feature fusion module are introduced,to improve feature representation.Second,a new classifier head—consisting of four convolutional layers and a fully connected layer—is proposed,to reduce the influence of information around the area of the defect.Third,to evaluate the proposed method,a dataset of aircraft skin defects was constructed,containing 276 images with a resolution of 960×720 pixels.Experimental results show that the proposed classifier head improves the detection and segmentation accuracy,for aircraft skin defect inspection,more effectively than the attention mechanism and feature fusion module.Compared with the Mask R-CNN and Mask Scoring R-CNN,the proposed method increased the segmentation precision by approximately 21%and 19.59%,respectively.These results demonstrate that the proposed method performs favorably against the other two methods of pixellevel aircraft skin defect detection.展开更多
The growing demand for electronic devices, smart devices, and the Internet of Things constitutes the primary driving force for marching down the path of decreased critical dimension and increased circuit intricacy of ...The growing demand for electronic devices, smart devices, and the Internet of Things constitutes the primary driving force for marching down the path of decreased critical dimension and increased circuit intricacy of integrated circuits. However, as sub-10 nm high-volume manufacturing is becoming the mainstream, there is greater awareness that defects introduced by original equipment manufacturer components impact yield and manufacturing costs. The identification, positioning, and classification of these defects, including random particles and systematic defects, are becoming more and more challenging at the 10 nm node and beyond.Very recently, the combination of conventional optical defect inspection with emerging techniques such as nanophotonics, optical vortices, computational imaging, quantitative phase imaging, and deep learning is giving the field a new possibility. Hence, it is extremely necessary to make a thorough review for disclosing new perspectives and exciting trends, on the foundation of former great reviews in the field of defect inspection methods. In this article, we give a comprehensive review of the emerging topics in the past decade with a focus on three specific areas:(a) the defect detectability evaluation,(b) the diverse optical inspection systems,and(c) the post-processing algorithms. We hope, this work can be of importance to both new entrants in the field and people who are seeking to use it in interdisciplinary work.展开更多
In integrated circuit(IC)manufacturing,fast,nondestructive,and precise detection of defects in patterned wafers,realized by bright-field microscopy,is one of the critical factors for ensuring the final performance and...In integrated circuit(IC)manufacturing,fast,nondestructive,and precise detection of defects in patterned wafers,realized by bright-field microscopy,is one of the critical factors for ensuring the final performance and yields of chips.With the critical dimensions of IC nanostructures continuing to shrink,directly imaging or classifying deep-subwavelength defects by bright-field microscopy is challenging due to the well-known diffraction barrier,the weak scattering effect,and the faint correlation between the scattering cross-section and the defect morphology.Herein,we propose an optical far-field inspection method based on the form-birefringence scattering imaging of the defective nanostructure,which can identify and classify various defects without requiring optical super-resolution.The technique is built upon the principle of breaking the optical form birefringence of the original periodic nanostructures by the defect perturbation under the anisotropic illumination modes,such as the orthogonally polarized plane waves,then combined with the high-order difference of far-field images.We validated the feasibility and effectiveness of the proposed method in detecting deep subwavelength defects through rigid vector imaging modeling and optical detection experiments of various defective nanostructures based on polarization microscopy.On this basis,an intelligent classification algorithm for typical patterned defects based on a dual-channel AlexNet neural network has been proposed,stabilizing the classification accuracy ofλ/16-sized defects with highly similar features at more than 90%.The strong classification capability of the two-channel network on typical patterned defects can be attributed to the high-order difference image and its transverse gradient being used as the network’s input,which highlights the polarization modulation difference between different patterned defects more significantly than conventional bright-field microscopy results.This work will provide a new but easy-to-operate method for detecting and classifying deep-subwavelength defects in patterned wafers or photomasks,which thus endows current online inspection equipment with more missions in advanced IC manufacturing.展开更多
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...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.展开更多
Additive manufacturing(AM) technology is considered one of the most promising manufacturing technologies in the aerospace and defense industries. However, AM components are known to have various internal defects, such...Additive manufacturing(AM) technology is considered one of the most promising manufacturing technologies in the aerospace and defense industries. However, AM components are known to have various internal defects, such as powder agglomeration, balling, porosity,internal cracks and thermal/internal stress, which can significantly affect the quality, mechanical properties and safety of final parts. Therefore, defect inspection methods are important for reducing manufactured defects and improving the surface quality and mechanical properties of AM components. This paper describes defect inspection technologies and their applications in AM processes. The architecture of defects in AM processes is reviewed. Traditional defect detection technology and the surface defect detection methods based on deep learning are summarized, and future aspects are suggested.展开更多
Automated X-ray defect inspection of occluded objects has been an essential topic in semiconductors,autonomous vehicles,and artificial intelligence devices.However,there are few solutions to segment occluded objects i...Automated X-ray defect inspection of occluded objects has been an essential topic in semiconductors,autonomous vehicles,and artificial intelligence devices.However,there are few solutions to segment occluded objects in the X-ray inspection efficiently.In particular,in the Ball Grid Array inspection of X-ray images,it is difficult to accurately segment the regions of occluded solder balls and detect defects inside solder balls.In this paper,we present a novel automatic inspection algorithm that segments solder balls,and detects defects fast and efficiently when solder balls are occluded.The proposed algorithm consists of two stages.In the first stage,the defective candidates or defects are determined through the following four steps:(i)image preprocessing such as noise removal,contrast enhancement,binarization,connected component,and morphology,(ii)limiting the inspec-tion area to the ball regions and determining if the ball regions are occluded,(iii)segmenting each ball region into one or more regions with similar gray values,and(iv)determining whether there are defects or defective candidates in the regions using a weighted sum of local threshold on local variance.If there are defective candidates,the determination of defects is finally made in the following stage.In the second stage,defects are detected using the automated inspection technique based on oblique computed tomography.The 3D precision inspection process is divided into four steps:(i)obtaining 360 projection images(one image per degree)rotating the object from 0 to 360 degrees,(ii)reconstructing a 3D image from the 360 projected images,(iii)finding the center slice of gravity for solder balls from the axial slice images in the z-direction,and getting the inspection intervals between the upper bound and the lower bound from the center slice,and(iv)finally determining whether there are defects in the averaged image of solder balls.The proposed hybrid algorithm is robust for segmenting the defects inside occluded solder balls,and improves the performance of solder ball segmentation and defect detection algorithm.Experimental results show an accuracy of more than 97%.展开更多
The manufacturing processes of casing rings are prone to multi-type defects such as holes,cracks,and porosity,so ultrasonic testing is vital for the quality of aeroengine.Conventional ultrasonic testing requires manua...The manufacturing processes of casing rings are prone to multi-type defects such as holes,cracks,and porosity,so ultrasonic testing is vital for the quality of aeroengine.Conventional ultrasonic testing requires manual analysis,which is susceptible to human omission,inconsistent results,and time-consumption.In this paper,a method for automated detection of defects is proposed for the ultrasonic Total Focusing Method(TFM)inspection of casing rings based on deep learning.First,the original datasets of defect images are established,and the Mask R-CNN is used to increase the number of defects in a single image.Then,the YOLOX-S-improved lightweight model is proposed,and the feature extraction network is replaced by Faster Net to reduce redundant computations.The Super-Resolution Generative Adversarial Network(SRGAN)and Convolutional Block Attention Module(CBAM)are integrated to improve the identification precision.Finally,a new test dataset is created by ultrasonic TFM inspection of an aeroengine casing ring.The results show that the mean of Average Precision(m AP)of the YOLOX-S-improved model reaches 99.17%,and the corresponding speed reaches 77.6 FPS.This study indicates that the YOLOX-S-improved model performs better than conventional object detection models.And the generalization ability of the proposed model is verified by ultrasonic B-scan images.展开更多
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...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.展开更多
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 wo...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.展开更多
An intelligent camera for surface defect inspection is presented which can pre-process the surface image of a rolled strip and pick defective areas out at a spead of 1 600 meters per minute. The camera is made up of a...An intelligent camera for surface defect inspection is presented which can pre-process the surface image of a rolled strip and pick defective areas out at a spead of 1 600 meters per minute. The camera is made up of a high speed line CCD, a 60 Mb/s CCD digitizer with correlated double sampling function, and a field programmable gate array(FPGA), which can quickly distinguish defective areas using a perceptron embedded in FPGA thus the data to be further processed would dramatically be reduced. Some experiments show that the camera can meet high producing speed, and reduce cost and complexity of automation surface inspection systems.展开更多
Steel truss suspension bridges are prone to developing defects after prolonged use.These defects may include corrosion of the main cable or the steel truss.To ensure the normal and safe functioning of the suspension b...Steel truss suspension bridges are prone to developing defects after prolonged use.These defects may include corrosion of the main cable or the steel truss.To ensure the normal and safe functioning of the suspension bridge,it is necessary to inspect for defects promptly,understand the cause of the defect,and locate it through the use of inspection technology.By promptly addressing defects,the suspension bridge’s safety can be ensured.The author has analyzed the common defects and causes of steel truss suspension bridges and proposed specific inspection technologies.This research is intended to aid in the timely discovery of steel truss suspension bridge defects.展开更多
An ideal printed circuit board(PCB)defect inspection system can detect defects and classify PCB defect types.Existing defect inspection technologies can identify defects but fail to classify all PCB defect types.This ...An ideal printed circuit board(PCB)defect inspection system can detect defects and classify PCB defect types.Existing defect inspection technologies can identify defects but fail to classify all PCB defect types.This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types.In the proposed algorithmic scheme,fuzzy cmeans clustering is used for image segmentation via image subtraction prior to defect detection.Arithmetic and logic operations,the circle hough transform(CHT),morphological reconstruction(MR),and connected component labeling(CCL)are used in defect classification.The algorithmic scheme achieves 100%defect detection and 99.05%defect classification accuracies.The novelty of this research lies in the concurrent use of CHT,MR,and CCL algorithms to accurately detect and classify all 14-known PCB defect types and determine the defect characteristics such as the location,area,and nature of defects.This information is helpful in electronic parts manufacturing for finding the root causes of PCB defects and appropriately adjusting the manufacturing process.Moreover,the algorithmic scheme can be integrated into machine vision to streamline the manufacturing process,improve the PCB quality,and lower the production cost.展开更多
Due to the merits of high inspection speed and long detecting distance, Ultrasonic Guided Wave（UGW） method has been commonly applied to the on-line maintenance of power transmission line. However, the guided wave pr...Due to the merits of high inspection speed and long detecting distance, Ultrasonic Guided Wave（UGW） method has been commonly applied to the on-line maintenance of power transmission line. However, the guided wave propagation in this structure is very complicated, leading to the unfavorable defect localization accuracy. Aiming at this situation, a high precision UGW technique for inspection of local surface defect in power transmission line is proposed. The technique is realized by adopting a novel segmental piezoelectric ring transducer and transducer mounting scheme, combining with the comprehensive characterization of wave propagation and circumferential defect positioning with multiple piezoelectric elements. Firstly, the propagation path of guided waves in the multi-wires of transmission line under the proposed technique condition is investigated experimentally. Next, the wave velocities are calculated by dispersion curves and experiment test respectively, and from comparing of the two results, the guided wave mode propagated in transmission line is confirmed to be F（1,1） mode. Finally, the axial and circumferential positioning of local defective wires in transmission line are both achieved, by using multiple piezoelectric elements to surround the stands and send elastic waves into every single wire. The proposed research can play a role of guiding the development of highly effective UGW method and detecting system for multi-wire transmission line.展开更多
The detection and classification of real-time surface defects play an important role in automotive sheet inspection and production. In this paper, an automatic surface inspection system （ASIS） based on signal proces...The detection and classification of real-time surface defects play an important role in automotive sheet inspection and production. In this paper, an automatic surface inspection system （ASIS） based on signal processing in Baosteel NO. 4 cold-rolled plant is briefly presented. We demonstrate that the strip surface defect properties such as image, type, pitch, and position can be accurately calculated and classified by the automatic surface inspection system. In the manufacturing of the high-quality cold-rolled strips, it is necessary that the real-time surface defects can be detected and transferred by the automatic surface inspection system combined with annealing lines and recoiling lines.展开更多
文摘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.
基金Supported by the National Natural Science Foundation of China （No. 60872096） and the Fundamental Research Funds for the Central Universities （No. 2009B31914）.
文摘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.
文摘The textile industry is one of the most important industries in Sri Lanka.In most of the textile garment factories the defects of the fabrics are detected manually.The manual textile quality control usually depends on eye inspection.Famously,human visual assessment is drawn-out,tiring,and an exhausting errand,including perception,consideration and experience to recognize the fault occurrence.The precision of human visual assessment declines with dull positions and vast schedules.Some of the time slow,costly,and sporadic review is the outcome.In this manner,the programmed automatic visual review safeguards both the fabric quality inspector and the quality.This examination has exhibited that Textile Defect Recognition System is fit for distinguishing fabrics’imperfections with endorsed exactness with viability.With some products 100%inspection is important to ensure the stipulated quality or standard.The classifications for the automated fabric inspection approaches are expanding as the work is vast and complex.According to the algorithm used,the texture analysis problem is classified into different approaches.They are Structural,spectral,model-based methods,Unfortunately,the optimal plan does not yet exist for these vast numbers of applied methods,as each of them has some advantages and disadvantages.
文摘The manual visual inspections of façade building defects are posing a high and increasing cost for building asset managers,particularly when inspections delay projects or require asset outages,visits to decommissioned sites or work within hazardous environments.This paper reports on the development,testing and delivery of a working mobile app prototype to facilitate the inspections and documentation of building facade condition monitoring.The work presented builds upon the development of an online platform for remote building inspection based on the integration of methodologies and tools,including VR(virtual reality),and digital photogrammetry to collect real-time data that support automated decision making.The mobile app:(i)allows the user to import 3D models and 2D building plans;(ii)provides the means of first-person exploration of models via a VR headset;and(iii)captures,records and catalogues images of façade defect types,and the date and time.An inspection case study was used to demonstrate and evaluate the mobile app prototype.The Building Inspector app allows building professionals to manage inspections and to track past and ongoing monitoring of the condition of building façades.
基金Tianjin Research Program of Application Foundation and Advanced Technology(No.14JCYBJC18600,No.14JCZDJC39700)National Key Scientific Instrument and Equipment Development Project(No.2013YQ17053903)
文摘Defect inspection of specular curved surface is a challenging job. Taking steel balls for example, a new method based on reflected pattern integrity recognition is put forward. The specular steel ball surface will totally reflect the patterns when it is placed inside a dome-shaped light source, whose inner wall is modified by patterns with certain regular. Distortion or intermittence of reflected pattern will occur at the defective part, which indicates the pattern has lost its integrity. Based on the integrity analysis of reflected pattern images？ surface defects can be revealed. In this paper, a set of concentric circles are used as the pattern and an image processing algorithm is customized to extract the surface defects. Results show that the proposed method is effective for the specular curved surface defect inspection
文摘Based on inspection data,the authors analyze and summarize the main types and distribution characteristics of tunnel structural defects.These defects are classified into three types:surface defects,internal defects,and defects behind the structure.To address the need for rapid detection of different defect types,the current state of rapid detection technologies and equipment,both domestically and internationally,is systematically reviewed.The research reveals that surface defect detection technologies and equipment have developed rapidly in recent years.Notably,the integration of machine vision and laser scanning technologies have significantly improved detection efficiency and accuracy,achieving crack detection precision of up to 0.1 mm.However,the non-contact rapid detection of internal and behind-the-structure defects remains constrained by hardware limitations,with traditional detection remaining dominant.Nevertheless,phased array radar,ultrasonic,and acoustic vibration detection technologies have become research hotspots in recent years,offering promising directions for detecting these challenging defect types.Additionally,the application of multisensor fusion technology in rapid detection equipment has further enhanced detection capabilities.Devices such as cameras,3D laser scanners,infrared thermal imagers,and radar demonstrate significant advantages in rapid detection.Future research in tunnel inspection should prioritize breakthroughs in rapid detection technologies for internal and behind-the-structure defects.Efforts should also focus on developing multifunctional integrated detection vehicles that can simultaneously inspect both surface and internal structures.Furthermore,progress in fully automated,intelligent systems with precise defect identification and real-time reporting will be essential to significantly improve the efficiency and accuracy of tunnel inspection.
基金National Natural Science Foundation of China(Nos.U2033201 and U1633105)。
文摘Skin defect inspection is one of the most significant tasks in the conventional process of aircraft inspection.This paper proposes a vision-based method of pixel-level defect detection,which is based on the Mask Scoring R-CNN.First,an attention mechanism and a feature fusion module are introduced,to improve feature representation.Second,a new classifier head—consisting of four convolutional layers and a fully connected layer—is proposed,to reduce the influence of information around the area of the defect.Third,to evaluate the proposed method,a dataset of aircraft skin defects was constructed,containing 276 images with a resolution of 960×720 pixels.Experimental results show that the proposed classifier head improves the detection and segmentation accuracy,for aircraft skin defect inspection,more effectively than the attention mechanism and feature fusion module.Compared with the Mask R-CNN and Mask Scoring R-CNN,the proposed method increased the segmentation precision by approximately 21%and 19.59%,respectively.These results demonstrate that the proposed method performs favorably against the other two methods of pixellevel aircraft skin defect detection.
基金funded by the National Natural Science Foundation of China(Grant Nos.52175509 and 52130504)the National Key Research and Development Program of China(2017YFF0204705)+1 种基金the Key Research and Development Plan of Hubei Province(2021BAA013)the National Science and Technology Major Project(2017ZX02101006-004)。
文摘The growing demand for electronic devices, smart devices, and the Internet of Things constitutes the primary driving force for marching down the path of decreased critical dimension and increased circuit intricacy of integrated circuits. However, as sub-10 nm high-volume manufacturing is becoming the mainstream, there is greater awareness that defects introduced by original equipment manufacturer components impact yield and manufacturing costs. The identification, positioning, and classification of these defects, including random particles and systematic defects, are becoming more and more challenging at the 10 nm node and beyond.Very recently, the combination of conventional optical defect inspection with emerging techniques such as nanophotonics, optical vortices, computational imaging, quantitative phase imaging, and deep learning is giving the field a new possibility. Hence, it is extremely necessary to make a thorough review for disclosing new perspectives and exciting trends, on the foundation of former great reviews in the field of defect inspection methods. In this article, we give a comprehensive review of the emerging topics in the past decade with a focus on three specific areas:(a) the defect detectability evaluation,(b) the diverse optical inspection systems,and(c) the post-processing algorithms. We hope, this work can be of importance to both new entrants in the field and people who are seeking to use it in interdisciplinary work.
基金funded by National Natural Science Foundation of China(Grant Nos.52130504,52305577,and 52175509)the Key Research and Development Plan of Hubei Province(Grant No.2022BAA013)+4 种基金the Major Program(JD)of Hubei Province(Grant No.2023BAA008-2)the Interdisciplinary Research Program of Huazhong University of Science and Technology(2023JCYJ047)the Innovation Project of Optics Valley Laboratory(Grant No.OVL2023PY003)the Postdoctoral Fellowship Program(Grade B)of China Postdoctoral Science Foundation(Grant No.GZB20230244)the fellowship from the China Postdoctoral Science Foundation(2024M750995)。
文摘In integrated circuit(IC)manufacturing,fast,nondestructive,and precise detection of defects in patterned wafers,realized by bright-field microscopy,is one of the critical factors for ensuring the final performance and yields of chips.With the critical dimensions of IC nanostructures continuing to shrink,directly imaging or classifying deep-subwavelength defects by bright-field microscopy is challenging due to the well-known diffraction barrier,the weak scattering effect,and the faint correlation between the scattering cross-section and the defect morphology.Herein,we propose an optical far-field inspection method based on the form-birefringence scattering imaging of the defective nanostructure,which can identify and classify various defects without requiring optical super-resolution.The technique is built upon the principle of breaking the optical form birefringence of the original periodic nanostructures by the defect perturbation under the anisotropic illumination modes,such as the orthogonally polarized plane waves,then combined with the high-order difference of far-field images.We validated the feasibility and effectiveness of the proposed method in detecting deep subwavelength defects through rigid vector imaging modeling and optical detection experiments of various defective nanostructures based on polarization microscopy.On this basis,an intelligent classification algorithm for typical patterned defects based on a dual-channel AlexNet neural network has been proposed,stabilizing the classification accuracy ofλ/16-sized defects with highly similar features at more than 90%.The strong classification capability of the two-channel network on typical patterned defects can be attributed to the high-order difference image and its transverse gradient being used as the network’s input,which highlights the polarization modulation difference between different patterned defects more significantly than conventional bright-field microscopy results.This work will provide a new but easy-to-operate method for detecting and classifying deep-subwavelength defects in patterned wafers or photomasks,which thus endows current online inspection equipment with more missions in advanced IC manufacturing.
文摘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.
基金financial support of the National Key R&D Program of China (Project Nos. 2017YFA0701200, 2016YFF0102003)the Shanghai Science and Technology Committee Innovation Grant (Grant Nos. 19ZR1404600, 17JC1400601)the Science Challenging Program of CAEP (Grant No. JCKY2016212A506-0106)。
文摘Additive manufacturing(AM) technology is considered one of the most promising manufacturing technologies in the aerospace and defense industries. However, AM components are known to have various internal defects, such as powder agglomeration, balling, porosity,internal cracks and thermal/internal stress, which can significantly affect the quality, mechanical properties and safety of final parts. Therefore, defect inspection methods are important for reducing manufactured defects and improving the surface quality and mechanical properties of AM components. This paper describes defect inspection technologies and their applications in AM processes. The architecture of defects in AM processes is reviewed. Traditional defect detection technology and the surface defect detection methods based on deep learning are summarized, and future aspects are suggested.
文摘Automated X-ray defect inspection of occluded objects has been an essential topic in semiconductors,autonomous vehicles,and artificial intelligence devices.However,there are few solutions to segment occluded objects in the X-ray inspection efficiently.In particular,in the Ball Grid Array inspection of X-ray images,it is difficult to accurately segment the regions of occluded solder balls and detect defects inside solder balls.In this paper,we present a novel automatic inspection algorithm that segments solder balls,and detects defects fast and efficiently when solder balls are occluded.The proposed algorithm consists of two stages.In the first stage,the defective candidates or defects are determined through the following four steps:(i)image preprocessing such as noise removal,contrast enhancement,binarization,connected component,and morphology,(ii)limiting the inspec-tion area to the ball regions and determining if the ball regions are occluded,(iii)segmenting each ball region into one or more regions with similar gray values,and(iv)determining whether there are defects or defective candidates in the regions using a weighted sum of local threshold on local variance.If there are defective candidates,the determination of defects is finally made in the following stage.In the second stage,defects are detected using the automated inspection technique based on oblique computed tomography.The 3D precision inspection process is divided into four steps:(i)obtaining 360 projection images(one image per degree)rotating the object from 0 to 360 degrees,(ii)reconstructing a 3D image from the 360 projected images,(iii)finding the center slice of gravity for solder balls from the axial slice images in the z-direction,and getting the inspection intervals between the upper bound and the lower bound from the center slice,and(iv)finally determining whether there are defects in the averaged image of solder balls.The proposed hybrid algorithm is robust for segmenting the defects inside occluded solder balls,and improves the performance of solder ball segmentation and defect detection algorithm.Experimental results show an accuracy of more than 97%.
基金supported by the Postdoctoral Fellowship Program of CPSF,China(No.GZC20232015)the China Postdoctoral Science Foundation(No.2024M752499)+3 种基金the Postdoctoral Project of Hubei Province,China(No.2024HBBHCXA076)the Wuhan East Lake New Technology Development Zone Open List Project,China(No.2022KJB128)the National Natural Science Foundation of China(No.51875428)the Fundamental Research Funds for the Central Universities,China(No.104972024RSCbs0013)。
文摘The manufacturing processes of casing rings are prone to multi-type defects such as holes,cracks,and porosity,so ultrasonic testing is vital for the quality of aeroengine.Conventional ultrasonic testing requires manual analysis,which is susceptible to human omission,inconsistent results,and time-consumption.In this paper,a method for automated detection of defects is proposed for the ultrasonic Total Focusing Method(TFM)inspection of casing rings based on deep learning.First,the original datasets of defect images are established,and the Mask R-CNN is used to increase the number of defects in a single image.Then,the YOLOX-S-improved lightweight model is proposed,and the feature extraction network is replaced by Faster Net to reduce redundant computations.The Super-Resolution Generative Adversarial Network(SRGAN)and Convolutional Block Attention Module(CBAM)are integrated to improve the identification precision.Finally,a new test dataset is created by ultrasonic TFM inspection of an aeroengine casing ring.The results show that the mean of Average Precision(m AP)of the YOLOX-S-improved model reaches 99.17%,and the corresponding speed reaches 77.6 FPS.This study indicates that the YOLOX-S-improved model performs better than conventional object detection models.And the generalization ability of the proposed model is verified by ultrasonic B-scan images.
基金funded by the Key Research and Development Project of China Academy of Railway Sciences Corporation Limited(2021YJ310).
文摘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.
文摘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.
文摘An intelligent camera for surface defect inspection is presented which can pre-process the surface image of a rolled strip and pick defective areas out at a spead of 1 600 meters per minute. The camera is made up of a high speed line CCD, a 60 Mb/s CCD digitizer with correlated double sampling function, and a field programmable gate array(FPGA), which can quickly distinguish defective areas using a perceptron embedded in FPGA thus the data to be further processed would dramatically be reduced. Some experiments show that the camera can meet high producing speed, and reduce cost and complexity of automation surface inspection systems.
文摘Steel truss suspension bridges are prone to developing defects after prolonged use.These defects may include corrosion of the main cable or the steel truss.To ensure the normal and safe functioning of the suspension bridge,it is necessary to inspect for defects promptly,understand the cause of the defect,and locate it through the use of inspection technology.By promptly addressing defects,the suspension bridge’s safety can be ensured.The author has analyzed the common defects and causes of steel truss suspension bridges and proposed specific inspection technologies.This research is intended to aid in the timely discovery of steel truss suspension bridge defects.
基金This research is supported by the National Research Council of Thailand(NRCT).Project ID:618211.
文摘An ideal printed circuit board(PCB)defect inspection system can detect defects and classify PCB defect types.Existing defect inspection technologies can identify defects but fail to classify all PCB defect types.This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types.In the proposed algorithmic scheme,fuzzy cmeans clustering is used for image segmentation via image subtraction prior to defect detection.Arithmetic and logic operations,the circle hough transform(CHT),morphological reconstruction(MR),and connected component labeling(CCL)are used in defect classification.The algorithmic scheme achieves 100%defect detection and 99.05%defect classification accuracies.The novelty of this research lies in the concurrent use of CHT,MR,and CCL algorithms to accurately detect and classify all 14-known PCB defect types and determine the defect characteristics such as the location,area,and nature of defects.This information is helpful in electronic parts manufacturing for finding the root causes of PCB defects and appropriately adjusting the manufacturing process.Moreover,the algorithmic scheme can be integrated into machine vision to streamline the manufacturing process,improve the PCB quality,and lower the production cost.
基金Supported by National Natural Science Foundation of China(Grant No51605229)Natural Science Foundation of Higher Education Institutions of Jiangsu Province,China(Grant No.16KJB460016)+1 种基金the“333”Project of Jiangsu Province,China(Grant No.BRA2015310)China Postdoctora Science Foundation(Grant No.2016M601844)
文摘Due to the merits of high inspection speed and long detecting distance, Ultrasonic Guided Wave（UGW） method has been commonly applied to the on-line maintenance of power transmission line. However, the guided wave propagation in this structure is very complicated, leading to the unfavorable defect localization accuracy. Aiming at this situation, a high precision UGW technique for inspection of local surface defect in power transmission line is proposed. The technique is realized by adopting a novel segmental piezoelectric ring transducer and transducer mounting scheme, combining with the comprehensive characterization of wave propagation and circumferential defect positioning with multiple piezoelectric elements. Firstly, the propagation path of guided waves in the multi-wires of transmission line under the proposed technique condition is investigated experimentally. Next, the wave velocities are calculated by dispersion curves and experiment test respectively, and from comparing of the two results, the guided wave mode propagated in transmission line is confirmed to be F（1,1） mode. Finally, the axial and circumferential positioning of local defective wires in transmission line are both achieved, by using multiple piezoelectric elements to surround the stands and send elastic waves into every single wire. The proposed research can play a role of guiding the development of highly effective UGW method and detecting system for multi-wire transmission line.
文摘The detection and classification of real-time surface defects play an important role in automotive sheet inspection and production. In this paper, an automatic surface inspection system （ASIS） based on signal processing in Baosteel NO. 4 cold-rolled plant is briefly presented. We demonstrate that the strip surface defect properties such as image, type, pitch, and position can be accurately calculated and classified by the automatic surface inspection system. In the manufacturing of the high-quality cold-rolled strips, it is necessary that the real-time surface defects can be detected and transferred by the automatic surface inspection system combined with annealing lines and recoiling lines.
