A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques

In this paper, we systematically review recent advances in surface inspection using computer vision andimage processing techniques, particularly those based on texture analysis methods. The aim is to reviewthe state-of-the-art techniques for the purposes of visual inspection and decision making schemes that areable to discriminate the features extracted from normal and defective regions. This field is so vast that itis impossible to cover all the aspects of visual inspection. This paper focuses on a particular but importantsubset which generally treats visual surface inspection as texture analysis problems. Other topics related tovisual inspection such as imaging system and data acquisition are out of the scope of this survey.The surface defects are loosely separated into two types. One is local textural irregularities which is themain concern for most visual surface inspection applications. The other is global deviation of colour and/ortexture, where local pattern or texture does not exhibit abnormalities. We refer this type of defects as shadeor tonality problem. The second type of defects have been largely neglected until recently, particularly whencolour imaging system has been widely used in visual inspection and where chromatic consistency plays animportant role in quality control. The emphasis of this survey though is still on detecting local abnormalities,given the fact that majority of the reported works are dealing with the first type of defects.The techniques used to inspect textural abnormalities are discussed in four categories, statistical approaches,structural approaches, filter based methods, and model based approaches, with a comprehensivelist of references to some recent works. Due to rising demand and practice of colour texture analysis inapplication to visual inspection, those works that are dealing with colour texture analysis are discussedseparately. It is also worth noting that processing vector-valued data has its unique challenges, which conventionalsurface inspection methods have often ignored or do not encounter.We also compare classification approaches with novelty detection approaches at the decision makingstage. Classification approaches often require supervised training and usually provide better performancethan novelty detection based approaches where training is only carried out on defect-free samples. However,novelty detection is relatively easier to adapt and is particularly desirable when training samples areincomplet

A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and human labor, but also brings about inefficiency and limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual industrial anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, publicly available datasets for industrial anomaly detection are introduced. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. Based on the current research framework, we point out the core issue that remains to be resolved and provide further improvement directions. Meanwhile, based on the latest technological trends, we offer insights into future research directions. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective

Fabric defect segmentation using multichannel blob detectors

The problem of automated defect detection in textured materials is investigated. A new algorithm based on multichannel filtering is presented. The texture features are extracted by filtering the acquired image using a filter bank consisting of a number of real Gabor functions, with multiple narrow spatial frequency and orientation channels. For each image, we propose the use of image fusion to multiplex the information from sixteen different channels obtained in four orientations. Adaptive degrees of thresholding and the associated effect on sensitivity to material impurities are discussed. This algorithm realizes large computational savings over the previous approaches and enables high-quality real-time defect detection. The performance of this algorithm has been tested thoroughly on real fabric defects, and experimental results have confirmed the usefulness of the approach.published_or_final_versio

Content-based image retrieval of museum images

Content-based image retrieval (CBIR) is becoming more and more important with the advance of multimedia and imaging technology. Among many retrieval features associated with CBIR, texture retrieval is one of the most difficult. This is mainly because no satisfactory quantitative definition of texture exists at this time, and also because of the complex nature of the texture itself. Another difficult problem in CBIR is query by low-quality images, which means attempts to retrieve images using a poor quality image as a query. Not many content-based retrieval systems have addressed the problem of query by low-quality images. Wavelet analysis is a relatively new and promising tool for signal and image analysis. Its time-scale representation provides both spatial and frequency information, thus giving extra information compared to other image representation schemes. This research aims to address some of the problems of query by texture and query by low quality images by exploiting all the advantages that wavelet analysis has to offer, particularly in the context of museum image collections. A novel query by low-quality images algorithm is presented as a solution to the problem of poor retrieval performance using conventional methods. In the query by texture problem, this thesis provides a comprehensive evaluation on wavelet-based texture method as well as comparison with other techniques. A novel automatic texture segmentation algorithm and an improved block oriented decomposition is proposed for use in query by texture. Finally all the proposed techniques are integrated in a content-based image retrieval application for museum image collections

Extraction of 3D Machined Surface Features and Applications.

In the modern production, the measurement of surface functions becomes more and more important. Most previous work on surface functional characterization are focused on surface tribological properties (roughness domain) and cover only a small area of a large engineering surface. Therefore, characterizing large engineering surface comprehensively and rapidly presents significant challenges. This research is focused on extracting 3D surface features from waviness domain and using these features to predict surface function and detect machining errors. In this research, an improved Gaussian filter is first designed to accurately extract 3D surface waviness from a large surface height map measured by a large field view interferometer. This filter technique enhances the performance of the standard Gaussian filter when applied to a surface which has large form distortion and many sharp peaks/valleys/noise. Following this, a 3D surface waviness feature of the machined workpiece is defined and applied to assess severe tool wear. Secondly, a two-channel filter bank diagram is developed that applies a 2D wavelet to decompose a 3D surface into multiple-scale subsurfaces. 3D surface features extracted from multiple-scale subsurfaces are then used to predict surface functions and detect machining faults. In the proposed surface decomposition process, two important issues: the elimination of border distortion and the transformation between the wavelet scale and its physical dimension are addressed. Applications of 2D wavelet decomposition to 3D surfaces are demonstrated using several automotive case studies, including abrupt tool breakage detection, chatter detection, cylinder head mating/sealing surface leak path detection, and transmission clutch piston surface non-clean up detection. Finally, a novel and automated surface defect detection and classification system for flat machined surfaces is designed. The purpose of this work is to extract microscopic surface anomalies and assign each anomaly to a surface defect type commonly found on the automotive machined surfaces. A “breadboard” version surface defect inspection system using multiple directional illuminations is constructed. Related image processing algorithms are developed to detect and identify 5 types of 2D or 3D surface defects (pore, 2D blemish, residue dirt, scratch, and gouge).Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78782/1/yiliao_1.pd

Reports on industrial information technology. Vol. 12

The 12th volume of Reports on Industrial Information Technology presents some selected results of research achieved at the Institute of Industrial Information Technology during the last two years.These results have contributed to many cooperative projects with partners from academia and industry and cover current research interests including signal and image processing, pattern recognition, distributed systems, powerline communications, automotive applications, and robotics

Non-destructive quality control of carbon anodes using modal analysis, acousto-ultrasonic and latent variable methods

La performance des cuves d’électrolyse utilisées dans la production d’aluminium primaire par le procédé Hall-Héroult est fortement influencée par la qualité des anodes de carbone. Celles-ci sont de plus en plus variables en raison de la qualité décroissante des matières premières (coke et braie) et des changements de fournisseurs qui deviennent de plus en plus fréquents afin de réduire le coût d’achat et de rencontrer les spécifications des usines. En effet, les défauts des anodes, tels les fissures, les pores et les hétérogénéités, causés par cette variabilité, doivent être détectés le plus tôt possible afin d’éviter d’utiliser des anodes défectueuses dans les cuves et/ou d’apporter des ajustements au niveau du procédé de fabrication des anodes. Cependant, les fabricants d’anodes ne sont pas préparés pour réagir à cette situation afin de maintenir une qualité d'anode stable. Par conséquent, il devient prioritaire de développer des techniques permettant d’inspecter le volume complet de chaque anode individuelle afin d’améliorer le contrôle de la qualité des anodes et de compenser la variabilité provenant des matières premières. Un système d’inspection basé sur les techniques d’analyse modale et d’acousto-ultrasonique est proposé pour contrôler la qualité des anodes de manière rapide et non destructive. Les données massives (modes de vibration et signaux acoustiques) ont été analysées à l'aide de méthodes statistiques à variables latentes, telles que l'Analyse en Composantes Principales (ACP) et la Projection sur les Structures Latentes (PSL), afin de regrouper les anodes testées en fonction de leurs signatures vibratoires et acousto-ultrasoniques. Le système d'inspection a été premièrement investigué sur des tranches d'anodes industrielles et ensuite testé sur plusieurs anodes pleine grandeur produites sous différentes conditions à l’usine de Alcoa Deschambault au Québec (ADQ). La méthode proposée a permis de distinguer les anodes saines de celles contenant des défauts ainsi que d’identifier le type et la sévérité des défauts, et de les localiser. La méthode acousto-ultrasonique a été validée qualitativement par la tomographie à rayon-X, pour les analyses des tranches d’anodes. Pour les tests réalisés sur les blocs d’anode, la validation a été réalisée au moyen de photos recueillies après avoir coupé certaines anodes parmi celles testées.The performance of the Hall-Héroult electrolysis reduction process used for the industrial aluminium smelting is strongly influenced by the quality of carbon anodes, particularly by the presence of defects in their internal structure, such as cracks, pores and heterogeneities. This is partly due to the decreasing quality and increasing variability of the raw materials available on the market as well as the frequent suppliers changes made in order to meet the smelter’s specifications and to reduce purchasing costs. However, the anode producers are not prepared to cope with these variations and in order to maintain consistent anode quality. Consequently, it becomes a priority to develop alternative methods for inspecting each anode block to improve quality control and maintain consistent anode quality in spite of the variability of incoming raw materials.A rapid and non-destructive inspection system for anode quality control is proposed based on modal analysis and acousto-ultrasonic techniques. The large set of vibration and acousto-ultrasonic data collected from baked anode materials was analyzed using multivariate latent variable methods, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), in order to cluster the tested anodes based on vibration and their acousto-ultrasonic signatures. The inspection system was investigated first using slices collected from industrial anodes and then on several full size anodes produced under different conditions at the Alcoa Deschambault in Québec (ADQ). It is shown that the proposed method allows discriminating defect-free anodes from those containing various types of defects. In addition, the acousto-ultrasonic features obtained in different frequency ranges were found to be sensitive to the defects severities and were able to locate them in anode blocks. The acousto-ultrasonic method was validated qualitatively using X-ray computed tomography, when studying the anode slices. The results obtained on the full size anode blocks were validated by means of images collected after cutting some tested anodes

Rolling contact fatigue failures in silicon nitride and their detection

The project investigates the feasibility of using sensor-based detection and processing systems to provide a reliable means of monitoring rolling contact fatigue (RCF) wear failures of silicon nitride in hybrid bearings. To fulfil this investigation, a decision was made early in the project to perform a series of hybrid rolling wear tests using a twin disc machine modified for use on hybrid bearing elements.The initial part of the thesis reviews the current understanding of the general wear mechanisms and RCF with a specific focus to determine the appropriate methods for their detection in hybrid bearings. The study focusses on vibration, electrostatic and acoustic emission (AE) techniques and reviews their associated sensing technologies currently deployed with a view of adapting them for use in hybrids. To provide a basis for the adaptation, an understanding of the current sensor data enhancement and feature extraction methods is presented based on a literature review.The second part describes the test equipment, its modifications and instrumentation required to capture and process the vibration, electrostatic and AE signals generated in hybrid elements. These were identified in an initial feasibility test performed on a standard twin disc machine. After a detailed description of the resulting equipment, the thesis describes the calibration tests aimed to provide base data for the development of the signal processing methods.The development of the signal processing techniques is described in detail for each of the sensor types. Time synchronous averaging (TSA) technique is used to identify the location of the signal sources along the surfaces of the specimens and the signals are enhanced by additional filtering techniques.The next part of the thesis describes the main hybrid rolling wear tests; it details the selection of the run parameters and the samples seeded with surface cracks to cover a variety of situations, the method of execution of each test run, and the techniques to analyse the results.The research establishes that two RCF fault types are produced in the silicon nitride rolling element reflecting essentially different mechanisms in their distinct and separate development; i) cracks, progressing into depth and denoted in this study as C-/Ring crack Complex (CRC) and ii) Flaking, progressing primarily on the surface by spalls. Additionally and not reported in the literature, an advanced stage of the CRC fault type composed of multiple and extensive c-cracks is interpreted as the result of induced sliding in these runs. In general, having reached an advanced stage, both CRC and Flaking faults produce significant wear in the steel counterface through abrasion, plastic deformation or 3-body abrasion in at least three possible ways, all of which are described in details