A VISION-BASED QUALITY INSPECTION SYSTEM FOR FABRIC DEFECT DETECTION AND CLASSIFICATION

Published ThesisQuality inspection of textile products is an important issue for fabric manufacturers. It is desirable to produce the highest quality goods in the shortest amount of time possible. Fabric faults or defects are responsible for nearly 85% of the defects found by the garment industry. Manufacturers recover only 45 to 65% of their profits from second or off-quality goods. There is a need for reliable automated woven fabric inspection methods in the textile industry. Numerous methods have been proposed for detecting defects in textile. The methods are generally grouped into three main categories according to the techniques they use for texture feature extraction, namely statistical approaches, spectral approaches and model-based approaches. In this thesis, we study one method from each category and propose their combinations in order to get improved fabric defect detection and classification accuracy. The three chosen methods are the grey level co-occurrence matrix (GLCM) from the statistical category, the wavelet transform from the spectral category and the Markov random field (MRF) from the model-based category. We identify the most effective texture features for each of those methods and for different fabric types in order to combine them. Using GLCM, we identify the optimal number of features, the optimal quantisation level of the original image and the optimal intersample distance to use. We identify the optimal GLCM features for different types of fabrics and for three different classifiers. Using the wavelet transform, we compare the defect detection and classification performance of features derived from the undecimated discrete wavelet and those derived from the dual-tree complex wavelet transform. We identify the best features for different types of fabrics. Using the Markov random field, we study the performance for fabric defect detection and classification of features derived from different models of Gaussian Markov random fields of order from 1 through 9. For each fabric type we identify the best model order. Finally, we propose three combination schemes of the best features identified from the three methods and study their fabric detection and classification performance. They lead generally to improved performance as compared to the individual methods, but two of them need further improvement

Automated Deform Detection For Automotive Body Panels Using Image Processing Techniques

The demand for automotive industry has been rapidly increasing as the number of consumer increases. In order to ensure the quality of their product, the manufacturers need to minimalize any deformation that occurs to their products. Early deformation detection on the automotive body panels manufactured must be conducted in order to rectify the problem. Automated deformation detection was designed to replace manual labour and this technique is found to be more accurate and effective. This thesis proposed a method to detect the deformation that occurs on the automotive body panel surface while in assembly lines. Three-dimensional data is acquired from the body panels as an input for deformation detection system. The data is converted in two-dimensional data image by using scatter data interpolation. Gradient filtering is used to identify the gradient energy value yield from the surface by using two types of kernels. Background illumination correction is implemented in order to reduce unwanted regions in the image. The prepared images undergo segmentation stage by recognizing the deformation in each threshold value by using Artificial Neural Network. The threshold value has been assigned with range between 0.0001 until 0.2000 where the threshold value is increased by 0.0001 in iteration.The Gabors’ Wavelet is used to extract the features of the segmented candidates and as the input for the artificial neural network. A fuzzy logic decision rule is used to classify the types of deformations that have been obtained from the artificial neural network outputs.The depth of the deformation is then computed by subtracting the maximum and minimum values of the segmented candidates. Several test units were purposely built with deforms in order to test the proposed method. The mean accuracy of the NN recognition with Gabors’ Features Extraction was recorded at 99.50 %. The segmentation on flat surface was recorded with lowest accuracy percentage of 68.81 %, then followed by the car door and the curved surface with accuracy percentage recorded at 70.39 % and 79.03 % respectively. The detection accuracy percentage was found to be 100 % where all the deformed location was able to be detected

HYPERSPECTRAL IMAGING AND PATTERN RECOGNITION TECHNOLOGIES FOR REAL TIME FRUIT SAFETY AND QUALITY INSPECTION

Hyperspectral band selection and band combination has become a powerful tool and have gained enormous interest among researchers. An important task in hyperspectral data processing is to reduce the redundancy of the spectral and spatial information without losing any valuable details that are needed for the subsequent detection, discrimination and classification processes. An integrated principal component analysis (PCA) and Fisher linear discriminant (FLD) method has been developed for feature band selection, and other pattern recognition technologies have been applied and compared with the developed method. The results on different types of defects from cucumber and apple samples show that the integrated PCA-FLD method outperforms PCA, FLD and canonical discriminant methods when they are used separately for classification. The integrated method adds a new tool for the multivariate analysis of hyperspectral images and can be extended to other hyperspectral imaging applications. Dimensionality reduction not only serves as the first step of data processing that leads to a significant decrease in computational complexity in the successive procedures, but also a research tool for determining optimal spectra requirement for online automatic inspection of fruit. In this study, the hyperspectral research shows that the near infrared spectrum at 753nm is best for detecting apple defect. When applied for online apple defect inspection, over 98% of good apple detection rate is achieved. However, commercially available apple sorting and inspection machines cannot effectively solve the stem-calyx problems involved in automatic apple defects detection. In this study, a dual-spectrum NIR/MIR sensing method is applied. This technique can effectively distinguish true defects from stems and calyxes, which leads to a potential solution of the problem. The results of this study will advance the technology in fruit safety and quality inspection and improve the cost-effectiveness of fruit packing processes

Error propagation in pattern recognition systems: Impact of quality on fingerprint categorization

The aspect of quality in pattern classification has recently been explored in the context of biometric identification and authentication systems. The results presented in the literature indicate that incorporating information about quality of the input pattern leads to improved classification performance. The quality itself, however, can be defined in a number of ways, and its role in the various stages of pattern classification is often ambiguous or ad hoc. In this dissertation a more systematic approach to the incorporation of localized quality metrics into the pattern recognition process is developed for the specific task of fingerprint categorization. Quality is defined not as an intrinsic property of the image, but rather in terms of a set of defects introduced to it. A number of fingerprint images have been examined and the important quality defects have been identified and modeled in a mathematically tractable way. The models are flexible and can be used to generate synthetic images that can facilitate algorithm development and large scale, less time consuming performance testing. The effect of quality defects on various stages of the fingerprint recognition process are examined both analytically and empirically. For these defect models, it is shown that the uncertainty of parameter estimates, i.e. extracted fingerprint features, is the key quantity that can be calculated and propagated forward through the stages of the fingerprint classification process. Modified image processing techniques that explicitly utilize local quality metrics in the extraction of features useful in fingerprint classification, such as ridge orientation flow field, are presented and their performance is investigated

Non-destructive evaluation of white striping and microbial spoilage of Broiler Breast Meat using structured-illumination reflectance imaging

Manual inspection is a prevailing practice for quality assessment of poultry meat, but it is labor-intensive, tedious, and subjective. This thesis aims to assess the efficacy of an emerging structured illumination reflectance imaging (SIRI) technique with machine learning approaches for assessing WS and microbial spoilage in broiler breast meat. Broiler breast meat samples were imaged by an in house-assembled SIRI platform under sinusoidal illumination. In first experiment, handcrafted texture features were extracted from direct component (DC, corresponding to conventional uniform illumination) and amplitude component (AC, unique to the use of sinusoidal illumination) images retrieved from raw SIRI pattern images build linear discriminant analysis (LDA) models for classifying normal and defective samples. A further validation experiment was performed using deep learning as a feature extractor followed by LDA. The third experiment was on microbial spoilage assessment of broiler meat, deep learning models were used to extract features from DC and AC images builds on classifiers. Overall, this research has demonstrated consistent improvements of AC over DC images in assessing WS and spoilage of broiler meat and that SIRI is a promising tool for poultry meat quality detection

Variable illumination and invariant features for detecting and classifying varnish defects

This work presents a method to detect and classify varnish defects on wood surfaces. Since these defects are only partially visible under certain illumination directions, one image doesn\u27t provide enough information for a recognition task. A classification requires inspecting the surface under different illumination directions, which results in image series. The information is distributed along this series and can be extracted by merging the knowledge about the defect shape and light direction

A THREE DIMENSIONAL (3D) VISION BASED DEFECT INSPECTION SYSTEM FOR GLUING APPLICATION

A Robot Vision System (RVS) is an adaptive and dynamic system that caters to a wide range of jobs where each involves a set of operations required to be done at a predetermined workstation. This research is focused on the development of a vision system to be integrated with KUKA arm robot. Pyramid object is used as a complimentary of the windscreen car as a model. It developed using plain cardboard with dimension of 15cm x 15cm. 2D matching application introduced to identify the characteristic of the object used in the system using CCD camera. Object used must be trained in training phase to create object template and used again in recognition phase for object classification. Then, two CCD cameras are used; placed at the top and front of the object to extract object’s edge location using Harris Point. Data extracted from it are used to find 3D coordination of each edge. Equation of straight line mostly used in this method to identify x, y and z coordinates. Data obtained from the system then used to give instruction to KUKA arm robot for gluing purposes. Pixel coordinates must be converted to robot coordinates for easier understanding by the robot. Three types of defect are trained as model templates and save to the memory known as bumper, gap and bubble defect. Each defect has special characteristic. Inspection system developed to identify problems occurs in gluing process. Template matching method used to call model trained in training phase to identify the uncertainties. Each defect occurs comes with its coordinate’s information for correction. Correction of defect consists of two phase; 1st CoD where correction is completed in first time and 2nd CoD where correction still need to be completed after the first correction. Data for all the process are recorded to prove that this algorithm made improvement with the previous research

Automatic surface defect quantification in 3D

Three-dimensional (3D) non-contact optical methods for surface inspection are of significant interest to many industrial sectors. Many aspects of manufacturing processes have become fully automated resulting in high production volumes. However, this is not necessarily the case for surface defect inspection. Existing human visual analysis of surface defects is qualitative and subject to varying interpretation. Automated 3D non-contact analysis should provide a robust and systematic quantitative approach. However, different 3D optical measurement technologies use different physical principles, interact with surfaces and defects in diverse ways, leading to variation in measurement data. Instrument s native software processing of the data may be non-traceable in nature, leading to significant uncertainty about data quantisation. Sub-millimetric level surface defect artefacts have been created using Rockwell and Vickers hardness testing equipment on various substrates. Four different non-contact surface measurement instruments (Alicona InfiniteFocus G4, Zygo NewView 5000, GFM MikroCAD Lite and Heliotis H3) have been utilized to measure different defect artefacts. The four different 3D optical instruments are evaluated by calibrated step-height created using slipgauges and reference defect artefacts. The experimental results are compared to select the most suitable instrument capable of measuring surface defects in robust manner. This research has identified a need for an automatic tool to quantify surface defect and thus a mathematical solution has been implemented for automatic defect detection and quantification (depth, area and volume) in 3D. A simulated defect softgauge with a known geometry has been developed in order to verify the implemented algorithm and provide mathematical traceability. The implemented algorithm has been identified as a traceable, highly repeatable, and high speed solution to quantify surface defect in 3D. Various industrial components with suspicious features and solder joints on PCB are measured and quantified in order to demonstrate applicability

Visualisation of Articular Cartilage Microstructure

This thesis developed image processing techniques enabling the detection and segregation of biological three dimensional images into its component features based upon shape and relative size of the features detected. The work used articular cartilage images and separated fibrous components from the cells and background noise. Measurement of individual components and their recombination into a composite image are possible. Developed software was used to analyse the development of hyaline cartilage in developing sheep embryos