A vision-based system for inspecting painted slates

Purpose – This paper describes the development of a novel automated vision system used to detect the visual defects on painted slates. Design/methodology/approach – The vision system that has been developed consists of two major components covering the opto-mechanical and algorithmical aspects of the system. The first component addresses issues including the mechanical implementation and interfacing the inspection system with the development of a fast image processing procedure able to identify visual defects present on the slate surface. Findings – The inspection system was developed on 400 slates to determine the threshold settings that give the best trade-off between no false positive triggers and correct defect identification. The developed system was tested on more than 300 fresh slates and the success rate for correct identification of acceptable and defective slates was 99.32 per cent for defect free slates based on 148 samples and 96.91 per cent for defective slates based on 162 samples. Practical implications – The experimental data indicates that automating the inspection of painted slates can be achieved and installation in a factory is a realistic target. Testing the devised inspection system in a factory-type environment was an important part of the development process as this enabled us to develop the mechanical system and the image processing algorithm able to perform slate inspection in an industrial environment. The overall performance of the system indicates that the proposed solution can be considered as a replacement for the existing manual inspection system. Originality/value – The development of a real-time automated system for inspecting painted slates proved to be a difficult task since the slate surface is dark coloured, glossy, has depth profile non-uniformities and is being transported at high speeds on a conveyor. In order to address these issues, the system described in this paper proposed a number of novel solutions including the illumination set-up and the development of multi-component image-processing inspection algorithm

A monitoring and diagnostic approach for stochastic textured surfaces

We develop a supervised-learning-based approach for monitoring and diagnosing texture-related defects in manufactured products characterized by stochastic textured surfaces that satisfy the locality and stationarity properties of Markov random fields. Examples of stochastic textured surface data include images of woven textiles; image or surface metrology data for machined, cast, or formed metal parts; microscopy images of material microstructure samples; etc. To characterize the complex spatial statistical dependencies of in-control samples of the stochastic textured surface, we use rather generic supervised learning methods, which provide an implicit characterization of the joint distribution of the surface texture. We propose two spatial moving statistics, which are computed from residual errors of the fitted supervised learning model, for monitoring and diagnosing local aberrations in the general spatial statistical behavior of newly manufactured stochastic textured surface samples in a statistical process control context. We illustrate the approach using images of textile fabric samples and simulated 2-D stochastic processes, for which the algorithm successfully detects local defects of various natures. Supplemental discussions, results, data and computer codes are available online

Prediction of sedimentation and bank erosion due to the construction of Kahang Dam

River impoundments continue to cause changes to the hydrological regimes of its host river. Thus, assessment and development of tools for better understanding of the sediment dynamics and riverbank erosion downstream the dam will be of great benefit to researchers and policymakers. The present research employs the use of field techniques and estimation models to improve the (i) prediction of suspended sediment concentration, (ii) monitoring riverbank erosion, and (iii) development of Riverbank Erosion Index (RbEI) for downstream Kahang Dam. This research used the Artificial Neural Network (ANN) and ANN with Autoregressive (AR) (NNETAR) in predicting suspended sediment concentration using sediment concentration, discharge and water level as inputs. Similarly, erosion pins were installed on four transects to monitor the riverbank for thirteen months. The results obtained for sediment concentration prediction clearly show that the R2 for NNETAR (0.885) have better value compared to ANN (0.695) even though the relationship between discharge and sediment concentration was weak, it outperforms the ANN. While based on the sediment rating curve (SRC) results, the same pattern was exhibited where the R2 for NNETAR show a greater value than ANN and SRC with R2 values of 0.695 and 0.451, respectively. Based on the observed results of quantified riverbank erosion, the most active transect eroded 1.747 mm/yr- while 0.657 mm/yr- is the least eroded. furthermore, the result reveals the maximum and minimum sediment contribution to the fluvial system from riverbank eroded to be 0.00743 tonnes/yr and 0.00148 tonnes/yr respectively. Lastly, by using discharge and percentage soil composition (sand and clay), a RbEI was developed by the adopted Equation 4.7 to estimate the status of riverbank erosion of River Kahang. Moreover, five classifications of erosion status were proposed, which can be used to describe the status and severity of the riverbank erosion. In conclusion, the estimates by the RbEI is expected to serve as basis for analysing and adopting river stabilisation and restoration design, which will be of importance to dam operators in making informed decisions regarding early warnings on the riverbank stability. Also, reliable sediment concentration estimation will assist in the development of catchment sediment budget which will give an insight into the effect of situating a dam on a river in terms of sediment supply and riverbank erosio

An Extended Review on Fabric Defects and Its Detection Techniques

In Textile Industry, Quality of the Fabric is the main important factor. At the initial stage, it is very essential to identify and avoid the fabrics faults/defects and hence human perception consumes lot of time and cost to reveal the fabrics faults. Now-a-days Automated Inspection Systems are very useful to decrease the fault prediction time and gives best visualizing clarity- based on computer vision and image processing techniques. This paper made an extended review about the quality parameters in the fiber-to-fabric process, fabrics defects detection terminologies applied on major three clusters of fabric defects knitting, woven and sewing fabric defects. And this paper also explains about the statistical performance measures which are used to analyze the defect detection process. Also, comparison among the methods proposed in the field of fabric defect detection

Adaptivna tehnika obrade slike za kontrolu kvalitete u proizvodnji keramičkih pločica

Automation of the visual inspection for quality control in production of materials with textures (tiles, textile, leather, etc.) is not widely implemented. A sophisticated system for image acquisition, as well as a fast and efficient procedure for texture analysis is needed for this purpose. In this paper the Surface Failure Detection (SFD) algorithm for quality control in ceramic tiles production is presented. It is based on Discrete Wavelet Transform (DWT) and Probabilistic Neural Networks (PNN) with radial basis. DWT provides a multi-resolution analysis, which mimics behavior of a human visual system and it extracts from the tile image the features important for failure detection. Neural networks are used for classification of the tiles with respect to presence of defects. Classification efficiency mainly depends on the proper choice of the training vectors for neural networks. For neural networks preparation we propose an automated adaptive technique based on statistics of the tiles defects textures. This technique enables fast adaptation of the SFD algorithm to different textures, which is important for automated visual inspection in the production of a new tile type.Automatizacija vizualne provjere za kontrolu kvalitete u proizvodnji materijala s teksturama (pločice, tekstil, kože, itd.) nije široko primijenjena u praksi. Za ovu namjenu potreban je sofisticirani sustav za snimanje slika, kao i brza i efikasna procedura za analizu tekstura. U ovom je radu predstavljen algoritam za detekciju površinskih oštećenja (SFD) u proizvodnji keramičkih pločica. Temelji se na diskretnoj valićnoj transformaciji (DWT) i probabilističkim neuronskim mrežama (PNN) s radijalnim bazama. DWT omogućava više-rezolucijsku analizu koja oponaša ljudski vizualni sustav i izdvaja iz slike pločice značajne za detekciju oštećenja. Neuronske mreže se koriste za klasifikaciju pločica ovisno o postojanju oštećenja. Efikasnost klasifikacije najviše ovisi o odgovarajućem odabiru vektora za učenje neuronskih mreža. Za pripremu neuronskih mreža predlažemo automatiziranu adaptivnu tehniku koja se temelji na statistici tekstura oštećenja na pločicama. Ova tehnika omogućava brzu adaptaciju SFD algoritma na različite teksture, što je posebno važno za automatiziranu vizualnu provjeru u proizvodnji novog tipa pločica

Adaptivna tehnika obrade slike za kontrolu kvalitete u proizvodnji keramičkih pločica

Automation of the visual inspection for quality control in production of materials with textures (tiles, textile, leather, etc.) is not widely implemented. A sophisticated system for image acquisition, as well as a fast and efficient procedure for texture analysis is needed for this purpose. In this paper the Surface Failure Detection (SFD) algorithm for quality control in ceramic tiles production is presented. It is based on Discrete Wavelet Transform (DWT) and Probabilistic Neural Networks (PNN) with radial basis. DWT provides a multi-resolution analysis, which mimics behavior of a human visual system and it extracts from the tile image the features important for failure detection. Neural networks are used for classification of the tiles with respect to presence of defects. Classification efficiency mainly depends on the proper choice of the training vectors for neural networks. For neural networks preparation we propose an automated adaptive technique based on statistics of the tiles defects textures. This technique enables fast adaptation of the SFD algorithm to different textures, which is important for automated visual inspection in the production of a new tile type.Automatizacija vizualne provjere za kontrolu kvalitete u proizvodnji materijala s teksturama (pločice, tekstil, kože, itd.) nije široko primijenjena u praksi. Za ovu namjenu potreban je sofisticirani sustav za snimanje slika, kao i brza i efikasna procedura za analizu tekstura. U ovom je radu predstavljen algoritam za detekciju površinskih oštećenja (SFD) u proizvodnji keramičkih pločica. Temelji se na diskretnoj valićnoj transformaciji (DWT) i probabilističkim neuronskim mrežama (PNN) s radijalnim bazama. DWT omogućava više-rezolucijsku analizu koja oponaša ljudski vizualni sustav i izdvaja iz slike pločice značajne za detekciju oštećenja. Neuronske mreže se koriste za klasifikaciju pločica ovisno o postojanju oštećenja. Efikasnost klasifikacije najviše ovisi o odgovarajućem odabiru vektora za učenje neuronskih mreža. Za pripremu neuronskih mreža predlažemo automatiziranu adaptivnu tehniku koja se temelji na statistici tekstura oštećenja na pločicama. Ova tehnika omogućava brzu adaptaciju SFD algoritma na različite teksture, što je posebno važno za automatiziranu vizualnu provjeru u proizvodnji novog tipa pločica

Quality grading of painted slates using texture analysis

This paper details the development of an automated vision-based solution for identification of paint and substrate defects on painted slates. The developed vision system consists of two major components. The first component of the system addresses issues including the mechanical implementation and interfacing the inspection system with the sensing and optical equipment. The second component involves the development of an image processing algorithm that is able to identify the visual defects present on the slate surface. The process of imaging the slate proved to be very challenging as the slate surface is darkly coloured and presents depth non-uniformities. Hence, a key issue for this inspection system was to devise an adequate illumination system that was able to accommodate challenges including the slates’ surface depth non-uniformities and vibrations generated by the conveying system. The visual defects are detected using a novel texture analysis solution where the greyscale (tonal characteristics) and texture information are embedded in a composite model. The developed inspection system was tested for robustness and experimental results are presented