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When a silicon wafer is cut into separate dies, their front and back sides might have chipping resulting in die cracks and yield loss. To prevent defect formation, silicon wafers should undergo optical inspection for evaluation of wafer chipping, its size, and its shape. This work proposes an automated method of image processing that includes die edge detection, die street search, and determination of chipping size and shape. Die edge search was done using an Otsu’s thresholding method. This technique was chosen out as the optimal of the five ones. The choice was based on the segmentation precision evaluation of two types of images: with sharp and blurred edges. Die street search was done using a developed algorithm capable of processing images with angular displacement. Chipping shape and size were calculated through die edge displacement from the die street. Based on the numerical evaluation of chipping size and shape, a chipping danger metric that may be used for detection of defective dies has been proposed. [ABSTRACT FROM AUTHOR]

Image processing methods for automated concrete crack detection are often challenged by binary noise. Noise removal methods decrease the false positive pixels of crack detection results, often at the cost of a reduction in true positives. This paper proposes a novel method for binary noise removal and segmentation of noisy concrete crack images. The method applies an area threshold before reducing the pixel groups in the image to a skeleton. Each skeleton is connected to its nearest neighbour before the remaining short skeletons in the image are removed using a length threshold. A morphological reconstruction follows to remove all elements in the original noisy image that do not intersect with the skeleton. Finally, pixel groups in close proximity to the endpoints of the pixel groups in the resulting image are reinstated. Testing was conducted on a dataset of noisy binary crack images; the proposed method (Skele-Marker) obtained recall, precision, intersection over union, and F1 score results of 77%, 91%, 72%, and 84%, respectively. Skele-Marker was compared to other methods found in literature and was found to outperform other methods in terms of precision, intersection over union and F1 score. The proposed method is used to make crack detection results more reliable, supporting the ever-growing demand for automated inspections of concrete structures. • We use image processing techniques to remove noise in binary concrete crack images. • False positives in binary concrete crack images can be reduced using our method. • We compare our concrete crack noise removal method to others found in literature. • Our method performed better than other concrete crack segmentation methods. [ABSTRACT FROM AUTHOR]

In large civil aircraft manufacturing, a time-consuming post-production process is the non-destructive inspection of wing panels. This work aims to address this challenge and improve the defects’ detection by performing automated aerial inspection using a small off-the-shelf multirotor. The UAV is equipped with a wide field-of-view camera and an ultraviolet torch for implementing non-invasive imaging inspection. In particular, the UAV is programmed to perform the complete mission and stream video, in real-time, to the ground control station where the defects’ detection algorithm is executed. The proposed platform was mathematically modelled in MATLAB/SIMULINK in order to assess the behaviour of the system using a path following method during the aircraft wing inspection. In addition, two defect detection algorithms were implemented and tested on a dataset containing images obtained during inspection at Airbus facilities. The results show that for the current dataset the proposed methods can identify all the images containing defects.

Abstract: In this article we introduce a visual measurement system intended for an automatic determination of material segregation in steel wires as a feedback quality control. Our system is based on an image processing which represents a non-destructive field in measurement area. Until our automated inspection system has been deployed, the measurement of segregation level was accomplished by a human inspector who represents inexact and subjective evaluation. Moreover our automated classification of individual steel wires is based on a certified official regulation. The mentioned automated classification of wires into pre-defined classes is carried out by a sequence of image processing steps. All these steps are progressively described in this article in-depth. [Copyright &y& Elsevier]

In this paper, we describe an ANN with a modified performance function which is used in an automatic inspection system of welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling, were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of geometrical features which characterise the defect shape and orientation was proposed and extracted between defect candidates. In a third stage, an artificial neural network for weld defect classification was used under a regularisation process with different architectures for the input layer and the hidden layer. Our aim is to analyse this ANN modifying the performance function using a γ parameter in its function, for different neurons in the input and hidden layer in order to obtain a better performance on the classification stage. The automatic system of recognition and classification proposed consists in detecting the four main types of weld defects met in practice plus the non-defect type. The results was compared with the aim to know the parameters that allow the best classification. The correlation coefficients, confusion matrix and the accuracy or the proportion of the total number of predictions that were correct was determined obtaining a value of 80% for the ANN using a modified performance function with a parameter γ=0.6. [ABSTRACT FROM AUTHOR]

Abstract: Random surface defects occur during the hot bar rolling of steels and are identified either by manual or by automated inspection techniques. Manual inspection techniques are purely based on the process knowledge of the inspector such as the location, type and kind of defects, and the primary sources of these defects. The automated techniques, to identify and classify the defects, rely on machine vision technologies and image processing algorithms based on support vector machines, wavelets, image processing and statistical inference. Both these approaches have their own advantages and limitations. To improve the accuracy of classification of these defects a process knowledge based support vector classification scheme is proposed (called PK-MSVM) which combines feature extraction task of automated inspection with the process knowledge. The defect observation data from the imaging sensor is transformed to include this process knowledge. Three attributes of the defects – length to width ratio, longitudinal location and transverse location- are used for this transformation are they are closely related to the thermo-mechanics of the rolling process. Different formulations of the multi-class support vector machines (MSVMs) are compared for this classification with or without process knowledge based transformation: one-against-one, one-against-all and Hastie’s algorithm of multi class SVM. It is found that the new approach (PK-MSVM) performs better than traditional MSVM for all the three formulations. For the best case, the performance sees a jump of more than 100%. Thus incorporating process knowledge in identification and classification does increase the reliability of inspection considerably. [Copyright &y& Elsevier]

The classic image processing method for flaw detection uses one image of the scene, or multiple images without correspondences between them. To improve this scheme, automated inspection using multiple views has been developed in recent years. This strategy's key idea is to consider as real flaws those regions that can be tracked in a sequence of multiple images because they are located in positions dictated by geometric conditions. In contrast, false alarms (or noise) can be successfully eliminated in this manner, since they do not appear in the predicted places in the following images, and thus cannot be tracked. This paper presents a method to inspect aluminum wheels using images taken from different positions using a method called automatic multiple view inspection. Our method can be applied to uncalibrated image sequences, therefore, it is not necessary to determine optical and geometric parameters normally present in the calibrated systems. In addition, to improve the performance, we designed a false alarm reduction method in two and three views called intermediate classifier block (ICB). The ICB method takes advantage of the classifier ensemble methodology by making use of feature analysis in multiple views. Using this method, real flaws can be detected with high precision while most false alarms can be discriminated. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we describe an adaptive-network-based fuzzy inference system to recognise welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling, were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of 12 geometrical features which characterise the defect shape and orientation was proposed and extracted between defect candidates. In a third stage, an adaptive-network-based fuzzy inference system (ANFIS) for weld defect classification was used. With the aim of obtaining the best performance to automate the process of the classification of defects, of all possible combinations without repetition of the 12 features chosen, four were used as input for the ANFIS. The results were compared with the aim to know the features that allow the best classification. The correlation coefficients were determined obtaining a minimum value of 0.84. The accuracy or the proportion of the total number of predictions that were correct was determined obtaining a value of 82.6%. [Copyright &y& Elsevier]

Abstract: In this paper, we describe an automatic detection system to recognise welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling, were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of geometrical features which characterise the defect shape and orientation was proposed and extracted between defect candidates. In a third stage, an artificial neural network (ANN) for weld defect classification was used. With the aim of obtaining the best performance of ANN three different methods for improving network generalisation was used. The results was compared with a method without generalisation. For the input layer, the principal component analysis technique was used in order to reduce the number of feature variables; and, for the hidden layer, a different number of neurons was used. [Copyright &y& Elsevier]

• Active InfraRed Thermography to monitor specimens in different laboratory tests. • Automation in the thermographic data processing and Segmentation of defect areas. • Subsurface defects simulate detachments and water infiltration in the specimens. • Good results are obtained regardless of the wetting degree and ceramic properties. • This work serves for a first and fast inspection in ceramic tiling building façades. InfraRed Thermography (IRT) has proven to be a valuable diagnostic tool due to its real-time, remote, and non-destructive operation yielding accurate detection of the position of defect areas in building façade ceramic tiling. Ceramic tiles coating building façades are widely used throughout the world because of their technical and aesthetic characteristics. However, the detachment of ceramic tiles and the water infiltration in deep layers are still common problems. So, this paper proposes active infrared thermography as a thermographic acquisition mode, in contrast to the common use of passive thermography, and segmentation of defect areas and automation in the thermal image processing as added values never before proposed in the ceramic tiling thermographic inspection. For that, specimens of ceramic tiling for building façades were tested under different laboratory conditions, with inserted corks (simulating detachments), and by injecting water into holes drilled in the back surfaces (simulating water infiltration), as defects. Good results have been obtained in all the tests, both in dry and wet conditions in the specimens and for surfaces with homogeneous and heterogeneous surface properties, serving the introduction of this workflow for a first and fast inspection in ceramic tiling building façades. Future research will work with the fine-tuning phase of the methodology by applying it to real case studies. [ABSTRACT FROM AUTHOR]

Abstract: Pipeline infrastructure is decaying at an accelerating rate due to reduced funding and insufficient quality control resulting in poor installation, little or no inspection and maintenance, and a general lack of uniformity and improvement in design, construction and operation practices. The current practice that is being followed to inspect the conditions of pipes is usually time consuming, tedious and expensive. It may also lead to diagnostic errors due to lack of concentration of human operators. Buried pipe defect classification is thus a practical and important pattern classification problem. These defects appear in the form of randomly shaped cracks and holes, broken joints and laterals, and others. This paper proposes a new neuro-fuzzy classifier that combines neural networks and concepts of fuzzy logic for the classification of defects by extracting features in segmented buried pipe images. A comparative evaluation of the K-NN, fuzzy K-NN, conventional backpropagation network, and proposed neuro-fuzzy projection network classifiers is carried out. Among the five neural methods implemented and tested, the proposed neuro-fuzzy classifier performs the best, with classification accuracies around 90% on real concrete pipe images. [Copyright &y& Elsevier]

Abstract: The detection of cracks in concrete infrastructure is a problem of great interest. In particular, the detection of cracks in buried pipes is a crucial step in assessing the degree of pipe deterioration for municipal and utility operators. The key challenge is that whereas joints and laterals have a predictable appearance, the randomness and irregularity of cracks make them difficult to model. Our previous work has led to a segmented pipe image (with holes, joints, and laterals eliminated) obtained by a morphological approach. This paper presents the development of a statistical filter for the detection of cracks in the pipes. We propose a two-step approach. The first step is local and is used to extract crack features from the buried pipe images; we present two such detectors as well as a method for fusing them. The second step is global and defines the cracks among the segment candidates by processes of cleaning and linking. The influences of the parameters on crack detection are studied and results are presented for various pipe images. [Copyright &y& Elsevier]

Abstract: The enormity of the problem of deteriorating pipeline infrastructure is widely apparent. Since a complete rebuilding of the piping system is not financially realistic, municipal and utility operators require the ability to monitor the condition of buried pipes. Thus, reliable pipeline assessment and management tools are necessary to develop long term cost effective maintenance, repair, and rehabilitation programs. In this paper a simple, robust and efficient image segmentation algorithm for the automated analysis of scanned underground pipe images is presented. The algorithm consists of image pre-processing followed by a sequence of morphological operations to accurately segment pipe cracks, holes, joints, laterals, and collapsed surfaces, a crucial step in the classification of defects in underground pipes. The proposed approach can be completely automated and has been tested on five hundred scanned images of buried concrete sewer pipes from major cities in North America. [Copyright &y& Elsevier]

In this paper, preprocessing and enhancement, segmentation techniques have been employed to adaptively and accurately segment the region from a given image. The proposed method has been designed to automatically detect defective region in component image using ANN. Initially the component images are smoothened by median filter and eliminate noise from the component image. Then the image is segmented by thresholding techniques. The 14 Haralick textural features are extracted from the segmented image using Spatial Grey level Co-occurrence matrices. The features are given as an input to the Back propagation network and they are classified as defective component and non defective component. An application of machine vision, incorporating neural networks, which aims to fully automate real-time inspection in component identification process, is described [ABSTRACT FROM AUTHOR]

The objective of this paper is to document part of a collaborative research program undertaken by the Centre for Computer Integrated Manufacture (CIM Centre) at Swinburne University of Technology in Hawthorn, Victoria, Australia and Australian Defence Industries (ADI) Ltd in the field of 'non-contact inspection'. This research program is one of two collaborative programs between the CIM Centre and ADI Ltd, related to inspecting the quality of components in an automated fashion, without the use of contacting sensors. There are a number of techniques currently being investigated at the CIM Centre, including lasers, vision, acoustic emission and X-ray based methods. This particular paper focuses on the research work undertaken by the first author in the detection of internal surface defects in forged, hollow cylindrical workpieces. The case study presented for consideration and discussion herein is related to the detection of cavity defects in forged pressure vessels. The paper provides a background into the range of alternative non-contact inspection techniques that are available (including lasers, ultrasonics, etc.) and the reasons why some of these failed to provide the functionality that was offered by the vision approach ultimately adopted. There is nothing unique about the application of vision systems in the detection of surface defects. However, in this research program, a number of factors have considerably complicated the application. These include lighting problems and the difficulties encountered in acquiring images within a confined cylindrical space. This paper documents the techniques that have been used to resolve some of these practical image acquisition and processing problems and the relative merits of each approach. The paper also examines some of the algorithmic problems involved in detection of particular surface anomalies in components through a range of techniques and concludes that the one most suited to the surfaces in the pressure vessel case-study is the so-called 'region-growing' approach. [ABSTRACT FROM AUTHOR]

In large civil aircraft manufacturing, a time-consuming post-production process is the non-destructive inspection of wing panels. This work aims to address this challenge and improve the defects' detection by performing automated aerial inspection using a small off-the-shelf multirotor. The UAV is equipped with a wide field-of-view camera and an ultraviolet torch for implementing non-invasive imaging inspection. In particular, the UAV is programmed to perform the complete mission and stream video, in real-time, to the ground control station where the defects' detection algorithm is executed. The proposed platform was mathematically modelled in MATLAB/SIMULINK in order to assess the behaviour of the system using a path following method during the aircraft wing inspection. In addition, two defect detection algorithms were implemented and tested on a dataset containing images obtained during inspection at Airbus facilities. The results show that for the current dataset the proposed methods can identify all the images containing defects. [ABSTRACT FROM AUTHOR]