Automatic Defect Inspection Research Articles

Fabric defect inspection based on lattice segmentation and Gabor filtering

Fabric defect inspection aims at detecting the defects presented on a patterned fabric surface to achieve high quality. However, visual inspection is challenging due to the diversity of the fabric patterns and defects. This paper presents an automatic defect inspection method which compares the similarities of semantic sub-images conformed to crystallographic groups called lattice. The lattices are automatically segmented based on morphological component analysis (MCA). The defect inspection is then formulated as a novel voting procedure depending on an ideal lattice artificially generated by investigating the distributions of responses given by convolving lattices with Gabor filters. The performance of the proposed method LSG (lattice segmentation assisted by Gabor filters) is evaluated on the databases of star- and box-pattern images. By comparing the resultant and ground-truth images, an overall detection rate of 0.975 is achieved, which is comparable with the state-of-the-art methods.

Automatic Defect Inspection with Adaptive Binarization and Bresenham's Algorithm for Spectacle Lens Products

Automatic Defect Inspection with Adaptive Binarization and Bresenham's Algorithm for Spectacle Lens Products

Light source optimization for automatic visual inspection of piston surface defects

This paper focused on the light source optimization for visual inspection of piston surface defects. A novel structured lighting system was developed to overcome the difficulties in the well-qualified imaging of tiny and random defects on the cylindrical smooth surface of components such as pistons. A polarized light-filtering based method was proposed to enhance the defect image contrast, enlarge the detection range, and suppress the noise of flawless area. It was both experimentally proven and theoretically proven effective by mathematical modeling of light intensity loss during light propagation. To analyze the influence of the pose and position of light sources on defect imaging quality, the system model for camera, light source, and workpiece was established, and the structure parameters of the lighting system were optimized to improve the brightness uniformity over the entire inspection region and lighting efficiency. Moreover, a soft lighting method was introduced as a subsidiary means of removing the surface noise at two ends of a piston. This method was found to be more effective compared with conventional software filtering algorithm. Finally, a case was examined. The defects on the automotive brake master piston surface were captured using the optimized light source system. A random sampling experiment with 600 specimens revealed that the false-positive rate was about 2.3%, and with no leak detection. The findings have been applied in the production, and it is expected to be referred in inspecting defects on other smooth cylindrical surfaces.

An Image Mosaic Method for Defect Inspection of Steel Rotary Parts

According to the features of the inspection images for the steel rotary parts with defects, a novel image mosaic method, using Scale Invariant Feature Transform (SIFT) feature tracking with purifying feature points based on slope probability measure and RANSAC algorithm, is proposed. First, the method preprocesses the captured sequence images, and then implements projection transformation for these images. Then, the registration parameters for two adjacency images, using the SIFT algorithm and removal algorithm of the pseudo matching feature point pairs based on slope probability measure and RANSAC algorithm, can be solved to mosaic the defect inspection images of the parts with enough characteristic information. On this basis, a hardware-based method is used to perform image stitching of the measured parts. Experimental results show that the method can produce a large number of the correct matching feature point pairs, and can get a seamless, clear surface image of the parts, which will settle the foundation for automatic accurate inspection of the surface defects on metal parts.

A novel method for surface defect inspection of optic cable with short-wave infrared illuminance

Intelligent on-line detection of cable quality is a crucial issue in optic cable factory, and defects on the surface of optic cable can dramatically depress cable grade. Manual inspection in optic cable quality cannot catch up with the development of optic cable industry due to its low detection efficiency and huge human cost. Therefore, real-time is highly demanded by industry in order to replace the subjective and repetitive process of manual inspection. For this reason, automatic cable defect inspection has been a trend. In this paper, a novel method for surface defect inspection of optic cable with short-wave infrared illuminance is presented. The special condition of short-wave infrared cannot only provide illumination compensation for the weak illumination environment, but also can avoid the problem of exposure when using visible light illuminance, which affects the accuracy of inspection algorithm. A series of image processing algorithms are set up to analyze cable image for the verification of real-time and veracity of the detection method. Unlike some existing detection algorithms which concentrate on the characteristics of defects with an active search way, the proposed method removes the non-defective areas of the image passively at the same time of image processing, which reduces a large amount of computation. OTSU algorithm is used to convert the gray image to the binary image. Furthermore, a threshold window is designed to eliminate the fake defects, and the threshold represents the considered minimum size of defects ε. Besides, a new regional suppression method is proposed to deal with the edge burrs of the cable, which shows the superior performance compared with that of Open-Close operation of mathematical morphological in the boundary processing. Experimental results of 10,000 samples show that the rates of miss detection and false detection are 2.35% and 0.78% respectively when ε equals to 0.5mm, and the average processing period of one frame image is 2.39ms. All the improvements have been verified in the paper to show the ability of our inspection method for optic cable.

Defect detection of jute fabric using image processing

Fabric inspection is important for maintaining the quality of jute fabric. Traditional inspection process for jute fabric defects is human visual inspection which is insufficient and costly. The quality of inspection process for jute fabrics is mainly performed manually. Mostly defects could be detected by the most highly trained inspectors. Manual defect detection is labour intensive, cumbersome, prone to errors and expensive. At present, the fabric defect detection in the jute industry is performed manually. In jute industry improved performance in the inspection of fabrics leads to good product quality and contributes to increased profitability and customer satisfaction. Hence the automatic fabric defect inspection is required to reduce the cost and time waste caused by defects. Automated defect detection is less labour intensive, more accurate, efficient and less costly. The detection of defects of moving jute fabric on inspection table can be identified using Image processing techniques. These image processing techniques are applied and for the input image of a defective fabric frame by frame, conversion into grey scale image, noise filtering, binary image conversion, thresholding are applied on each image of video and the output is obtained in real time. In real time, output will be display the marks on defect area, defect percentage and defect concentration graph of capture length of fabric on inspection table.

An Automated Sensing System for Steel Bridge Inspection Using GMR Sensor Array and Magnetic Wheels of Climbing Robot

Corrosion is one of the main causes of deterioration of steel bridges. It may cause metal loss and fatigue cracks in the steel components, which would lead to the collapse of steel bridges. This paper presents an automated sensing system to detect corrosion, crack, and other kinds of defects using a GMR (Giant Magnetoresistance) sensor array. Defects will change the relative permeability and electrical conductivity of the material. As a result, magnetic field density generated by ferromagnetic material and the magnetic wheels will be changed. The defects are able to be detected by using GMR sensor array to measure the changes of magnetic flux density. In this study, magnetic wheels are used not only as the adhesion device of the robot, but also as an excitation source to provide the exciting magnetic field for the sensing system. Furthermore, compared to the eddy current method and the MFL (magnetic flux leakage) method, this sensing system suppresses the noise from lift-off value fluctuation by measuring the vertical component of induced magnetic field that is perpendicular to the surface of the specimen in the corrosion inspection. Simulations and experimental results validated the feasibility of the system for the automated defect inspection.

A cost-effective and automatic surface defect inspection system for hot-rolled flat steel

Defective steel brings economic and commercial reputation losses to the hot-strip manufacturers, and one of the main difficulties in using machine-vision-based technique for steel surface inspection is time taken to process the massive images suffering from uneven illumination. This paper develops a modular and cost-effective AOI system for hot-rolled flat steel in real time. Firstly, a detailed system topology is constructed according to the design goals covering the vast majority of steel mills, lighting setup and typical defect patterns are presented as well. Secondly, the image enhancement method is designed to overcome the uneven-lighting, over- or under-exposure. Thirdly, the defect detection algorithm is developed based on variance, entropy and average gradient derived from non-overlapping 32×32 pixel blocks of steel surface images. Fourthly, the proposed algorithms are implemented on FPGA in parallel to improve the inspection speed. Finally, 18,071 contiguous images (4096×1024 pixel) acquired from 7 defective steel rolls have been inspected by the realized AOI system to evaluate the performance. The experimental results show that the proposed method is speedy and effective enough for real applications in the hot-rolled steel manufacturing, with 92.11% average accuracy while 5.54% false-negative rate. HighlightsDetailed system topology is constructed according to the design goals covering the vast majority of steel mills.A new dynamic image enhancement method is designed to overcome the uneven-lighting, over- or under-exposure.A defect detection algorithm with adaptive thresholding mechanism is developed based on image block variance, entropy and average gradient.The inspection speed of the proposed algorithms running on FPGA is evaluated.Testing procedures and evaluation results from the applied hot-rolling mill are discussed.

An Intelligent and Confident System for Automatic Surface Defect Quantification in 3D

Automatic surface defect inspection within mass production of high-precision components is growing in demand and requires better measurement and automated analysis systems. Many manufacturing industries may reject manufactured parts that exhibit even minor defects, because a defect might result in an operational failure at a later stage. Defect quantification (depth, area and volume) is a key element in quality assurance in order to determine the pass or failure criterion of manufactured parts. Existing human visual analysis of surface defects is qualitative and subjective to varying interpretation. Non-contact and three dimensional (3D) analyses should provide a robust and systematic quantitative approach for defect analysis. Various 3D measuring instruments generate point cloud data as an output, although they work on different physical principles. Instrument’s native software processing of point cloud data is often subject to issues of repeatability and may be non-traceable causing significant concern with data confidence.This work reports the development of novel traceable surface defect artefacts produced using the Rockwell hardness test equipment on flat metal plate, and the development of a novel, traceable, repeatable, mathematical solution for automatic defect detection and quantification in 3D. Moreover, in order to build-up the confidence in automatic defect analysis system and generated data, mathematical simulated defect artefacts (soft-artefact) have been created. This is then extended to a surface defect on a piston crown that is measured and quantified using a parallel optical coherence tomography instrument integrated with 6 axis robot. The results show that surface defect quantification using implemented solution is efficient, robust and more repeatable than current alternative approaches.

Defect inspection for TFT-LCD images based on the low-rank matrix reconstruction

Surface defect inspection of TFT-LCD panels is a critical task in LCD manufacturing. In this paper, an automatic defect inspection method based on the low-rank matrix reconstruction is proposed. The textured background of the LCD image is a low-rank matrix and the foreground image with defects can be treated as a sparse matrix. By utilizing the Inexact Augmented Lagrange Multipliers (IALM) algorithm, the segmentation of a LCD image can be converted into the reconstruction of a low-rank matrix with a fraction of its entries arbitrarily corrupted. This low-rank matrix reconstruction problem can be exactly solved via convex optimization that minimizes a combination of the nuclear norm and the l 1 -norm. Also, adaptive parameter selection strategy is proposed by conducting deep analysis on the IALM algorithm, which improves the generality of the IALM algorithm for different defect types. Experiment results show that our inspection algorithm is robust for the defect shapes and types under different illumination conditions. The shapes and edges of defect areas in the LCD images can be well preserved and segmented from textured background by our detection algorithm.

전자내시경을 활용한 공압실린더 튜브 내면의 결함 자동검사시스템 개발

The system developed inspects automatically defects existing on an inner surface of anodically treated aluminum cylinder-tubes. This system consists of automatic inspection software, and electronic endoscope and a conveyor moving device. By applying the optimal brightness conditions to searching for various defects on the inner surface of cylinder tube, the recognition rate of major defects, such as scratch, oxide and weld line reached 99%. If the present visual inspection process is replaced with the automatic defects inspection system, the physical fatigue of the operator could be reduced and the productivity could be increased. The automatic inspection system developed could also improve the quality of the products

TFT-LCD Spot-Type Defect Detection in Module Process

In this paper we present a global approach for the automatic inspection of spot-type defects in TFT-LCD module process. The proposed method is based on a global image reconstruction scheme using the Fourier transform. Since TFT-LCD surface has the statistical and periodic texture, the isotropic spread of frequency components in the power spectrum space forms the shape approximate to a circle. By computing the convolution between the Fourier transform result and a ideal low pass filter (ILPF) , we can remove the TFT-LCD periodic and repetitive patterns using the inverse Fourier transform. In the restored image, the global structurally textured background is eliminated and the spot-type defects are preserved. This converts the difficult defect detection in TFT-LCD images into a simple segmentation problem. The spot-type defects are classified into general point defects and spot Mura defects .The experimental results from point defects and spot Mura defects have shown the efficacy of the proposed method.

Defect inspection and extraction of the mobile phone cover glass based on the principal components analysis

Surface defect inspection is an important part of quality control in mobile phone cover glass manufacturing. The traditional method is usually carried out manually by experienced inspectors and thus lacks sufficient efficiency and accuracy. In this paper, an automatic defect inspection system based on the principal components analysis is proposed for five typical cover glass defects: scratch, crack, deformation, edge broken, and angle cutting. This inspection system is robust for the defect shapes and obtains high recognition accuracy. The inspection system includes three parts: pre-processing, PCA-based defect recognition, and defect edge extraction. After pre-processing, most of noise and outliers are eliminated and the pixels of defects in the image are well enhanced for inspection and recognition. The eigen-defect matrix is constructed to characterize the variation between defect images. Additionally, time consumption in constructing the eigen-defect matrix is also discussed. The experimental results show that the inspection system has achieved high accuracy for inspection and recognition.

Automated defect and contaminant inspection of HVAC duct

To sustain acceptable indoor air quality in a building, it is essential to frequently inspect and clean the Heating, Ventilation and Air-Conditioning (HVAC) ductwork. Nowadays the condition inspection is mostly conducted manually according to the video acquired by a pipeline robot. This situation has been significantly resulting in subjectivity, high-cost and inefficiency for HVAC ductwork cleaning and maintenance. In this paper an automatic defect and contaminant inspection system of HVAC duct is developed. The system consists of an infrared-CCD diagnosis device and a novel supervised method for duct inspection by cascading seeded k-means and C4.5 decision tree. The seeded k-means feature-clustering method first partitions the features of training instances into k clusters using Euclidean distance similarity. C4.5 decision tree is then used to refine the decision boundaries by learning the subgroups within the cluster. Finally the decisions of the k-means and C4.5 methods are combined to achieve the inspection results. To improve the classification performance on the minority classes as well as reduce the computation load during the process, Tabu search is employed for the feature selection and the cost-sensitive function is introduced into Tabu search. Experimental results on real-world data sets demonstrate that the proposed system is effective and efficient in inspecting the condition of HVAC ductwork.

Automatic inspection system of surface defects on optical IR-CUT filter based on machine vision

The paper presents an automatic surface defects inspection system for optical Infrared Cut-off (IR-CUT) filter, which is applied in all kinds of color cameras and video devices. The system involves illumination and imaging module, moving module, flipping module and machine vision algorithm. To highlight all the defected regions, the improved dark-field illumination technique is utilized in the imaging module. In order to accurately localize the region of optical IR-CUT filter in the captured image, stationary wavelet transform (SWT) is introduced to template matching algorithm. The introduction of SWT provides a more accurate estimate of the variances in the image and further facilitates the identification of the defected regions. The defects extraction method in this paper avoids the use of complicated learning process from a set of samples. Convexity theory is implemented on the algorithm of defects classification of edge crack. Experimental results on a variety of optical IR-CUT filter samples, including non-defective samples, samples with defects of stain, scratch and edge crack, have shown the efficiency (1.05s per sample) and accuracy (96.44%) of the proposed system. Moreover, defects extraction performances of different filters are compared in this paper. The research and application of the system will greatly liberate the human workforce and inspire ideas to detect the defects of some other small optical elements.

Pretest Gap Mura on TFT LCDs Using the Optical Interference Pattern Sensing Method and Neural Network Classification

Recently, thin-film transistor liquid crystal displays (TFT LCDs) have had a high demand in the market, which entails careful product quality control and more stringent defect detection procedures. A good defect detection rate is the basic requirement of the quality control process. The use of conventional human visual inspection methods to find the defects in TFT LCDs is simply not accurate enough and consumes a large amount of resources. An automatic defect inspection method is thus necessary for this industry; to find the defects, the type of defects needs to be recognized as well. Here, we propose an inspection procedure based on the optical interference pattern sensing method to find the interference fringes and then use the image processing to enhance the contrast of the interference fringes, thereby increasing the recognition rate for the latter process. The neural network method is used to learn about and identify the defects and their types. This paper focuses on the mura defect inspection and classification method. Before the learning process has begun, the mean squared error was roughly three, but after neural network retraining of these samples, the results showed that the mean squared error was less than 0.01. The defective panels can be sorted out using this method so that the next processing and waste of materials can be avoided.

가버 웨이블렛 변환 및 DCT를 이용한 자동 TFT-LCD 패널 얼룩 검출

최근 다양한 형태의 TFT-LCD의 수요가 증가함에 따라 LCD 생산 과정에서 얼룩결함을 검사하는 기술에 대한 관심이 높아지고 있다. 본 논문에서는 가버 웨이블렛 변환(Gabor wavelet transform) 및 이산 코사인 변환(Discrete Cosine Transform, DCT)을 이용한 TFT-LCD 패널의 얼룩(mura)을 자동으로 검출하는 방법을 제안한다. 제안한 방법은 DCT 변환 기반의 TFT-LCD 패널 영상의 참조 패널 영상을 생성한다. 원 영상과 생성된 참조 패널 영상에 대해서 실수 가버 웨이블렛 변환(real gabor wavelet transform)을 적용하여 패널 영상에 포함되어 있는 얼룩 결함을 검출하는데 방해가 되는 텍스쳐 정보를 제거하고 변환 영상간의 차영상을 이용하여 제거 결함 영역을 추출한다. 추출된 영역에 대해서는 정량적 평가 과정을 통해 보다 정확한 얼룩 검출을 수행한다. 실험결과는 제안한 방법이 기존의 방법에 비해 보다 정확하고 효율적으로 얼룩을 검출하는 것을 보여준다. Recently, mura defect inspection techniques are receiving attention in LCD production procedure since demands of TFT-LCD are growing. In this paper, we propose an automatic mura defect inspection method using gabor wavelet transform and DCT. First, we generate a reference panel image using DCT based method. For original panel image and generated reference panel image, we apply a gabor wavelet transform to eliminate texture information in images. Then, we extract mura defect regions from the difference image between gabor wavelet transform image of original panel and generated reference panel image. Finally, all mura defect regions are quantified to detect accurate mura defects. Experimental results show that our method is more accurate and efficient than previous methods.

Automatic defect inspection system of colour filters using Taguchi-based neural network

A coloured filter is a critical part of an LCD panel, especially to present a high quality colour display. At present, the defect detection of colour filters is conducted by manual inspection in the final product stage. However, poor detection efficiency and subjective judgment of manual inspection undermine accuracy. Therefore, this study applied image processing technology and the neural network to detect surface defects of colour filters in order to prevent losses arising from incorrect detection, lower production costs, and effectively improve yield. A back-propagation neural network (BPNN) classifier was selected to train the features. The results showed that the proposed method can be successfully applied in defect detection of colour filters to reduce artificial detection errors. In addition, the Taguchi method was used with BPNN to save time searching optimal learning parameters by the trial and error method, which achieves faster convergence, smaller convergent errors and better recognition rate. The results proved that the root-mean-square error (RMSE) of the Taguchi-based BPNN at final convergence is 0.000254, and recognition rate reaches 94%. Therefore, the proposed method has good effects in detecting the micro defects of a colour filter panel.

Automatic Defect Detection of Patterned Fabric by using RB Method and Independent Component Analysis

Automatic fabric inspection is important for maintain the fabric quality. For a long time the fabric defects inspection process is still carried out with human visual inspection, and thus, insufficient and costly. Hence the automatic fabric defect inspection is required to reduce the cost and time waste caused by defects. The development of fully automated web inspection system requires robust and efficient fabric defect detection algorithms. The detection of local fabric defects is one of the most intriguing problems in computer vision. Texture analysis plays an important role in the automated visual inspection of texture images to detect their defects. The main aim of this study is to find independent components of the Regular Bands method of the patterned fabric images for the purpose of defect detection in this paper, Independent Component Analysis (ICA) is the proposed method that solves the problem of defect detection in patterned fabrics prior to Regular Bands (RB) method. Patterned fabric is built on the repetitive unit of its design. RB is an existing method that is based on periodicity. The proposed method ICA along with RB method tries to improve the efficiency and quality of the fabric with in less time.

TFT-LCD 패널 검사를 위한 지역적 분별에 기반한 결함 영역 분할 알고리즘

This paper proposes an image segmentation for a vision-based automated defect inspection system on surface image of TFT-LCD(Thin Film Transistor Liquid Crystal Display) panels. TFT-LCD images have non-uniform brightness, which is hard to finding defective regions. Although there are several methods or proposed algorithms, it is difficult to divide the defect with high reliability because of non-uniform properties in the image. Kamel and Zhao disclosed a method which based on logical stage algorithm for segmentation of graphics and character. This method is a one of the local segmentation method that has a advantage. It is that characters and graphics are well segmented in an image which has non-uniform property. As TFT-LCD panel image has a same property, so this paper proposes new algorithm to segment regions of defects based on Kamel and Zhao`s algorithm. Our algorithm has an advantage that there are a few ghost objects around the defects. We had experiments to prove performance in real TFT-LCD panel images, and comparing with the FFT(Fast Fourier Transform) method which is used a bandpass filter.