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

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

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

Inteligentni sustav strojnog vida za automatiziranu kontrolu kvalitete keramičkih pločica

U članku je prikazan automatizirani sustav za vizualnu kontrolu kvalitete keramičkih pločica uporabom strojnog računalnog vida. Proces proizvodnje keramičkih pločica u gotovo svim svojim fazama zadovoljavajuće je automatiziran, osim u fazi kontrole kvalitete, na kraju procesa. Kvaliteta keramičkih pločica provjerava se i ocjenjuje postupcima vizualne provjere kvalitete, gdje se ljudski čimbenik nastoji zamijeniti sustavom strojnog računalnog vida u funkciji povećanja kvalitete i povećanja efikasnosti proizvodnje. Kvaliteta keramičkih pločica definirana je dimenzijama i površinskim značajkama. Predstavljeni sustav strojnog vida analizira geometrijske i površinske značajke te odlučuje o kvaliteti keramičkih pločica na temelju navedenih značajki uporabom klasifikatora s neuronskom mrežom. Predstavljene su također i metode koje poboljšavaju izdvajanje geometrijskih i površinskih svojstava. Potvrđena je efikasnost obradnih algoritama i primjena neuronskog klasifikatora kao zamjene za vizualnu kontrolu kvalitete ljudskim vidom

Inteligentni sustav strojnog vida za automatiziranu kontrolu kvalitete keramičkih pločica

Intelligent system for automated visual quality control of ceramic tiles based on machine vision is presented in this paper. The ceramic tiles production process is almost fully and well automated in almost all production stages with exception of quality control stage at the end. The ceramic tiles quality is checked by using visual quality control principles where main goal is to successfully replace man as part of production chain with an automated machine vision system to increase production yield and decrease the production costs. The quality of ceramic tiles depends on dimensions and surface features. Presented automated machine vision system analyzes those geometric and surface features and decides about tile quality by utilizing neural network classifier. Refined methods for geometric and surface features extraction are presented also. The efficiency of processing algorithms and the usage of neural networks classifier as a substitution for human visual quality control are confirmed.U članku je prikazan automatizirani sustav za vizualnu kontrolu kvalitete keramičkih pločica uporabom strojnog računalnog vida. Proces proizvodnje keramičkih pločica u gotovo svim svojim fazama zadovoljavajuće je automatiziran, osim u fazi kontrole kvalitete, na kraju procesa. Kvaliteta keramičkih pločica provjerava se i ocjenjuje postupcima vizualne provjere kvalitete, gdje se ljudski čimbenik nastoji zamijeniti sustavom strojnog računalnog vida u funkciji povećanja kvalitete i povećanja efikasnosti proizvodnje. Kvaliteta keramičkih pločica definirana je dimenzijama i površinskim značajkama. Predstavljeni sustav strojnog vida analizira geometrijske i površinske značajke te odlučuje o kvaliteti keramičkih pločica na temelju navedenih značajki uporabom klasifikatora s neuronskom mrežom. Predstavljene su također i metode koje poboljšavaju izdvajanje geometrijskih i površinskih svojstava. Potvrđena je efikasnost obradnih algoritama i primjena neuronskog klasifikatora kao zamjene za vizualnu kontrolu kvalitete ljudskim vidom

A machine vision system for quality grading of painted slates

The major aim of this chapter is to detail the technology associated with a novel industrial inspection system that is able to robustly identify the visual defects present on the surface of painted slates. The development of a real-time automated slate inspection system proved to be a challenging task since the surface of the slate is painted with glossy dark colours, the slate is characterised by depth profile non-uniformities and it is transported at the inspection line via high-speed conveyors. In order to implement an industrial compliant system, in our design we had to devise a large number of novel solutions including the development of a full customised illumination set-up and the development of flexible image-processing procedures that can accommodate the large spectrum of visual defects that can be present on the slate surface and the vibrations generated by the slate transport system. The developed machine vision system has been subjected to a thorough robustness evaluation and the reported experimental results indicate that the proposed solution can be used to replace the manual procedure that is currently used to grade the painted slates in manufacturing environments

Colour Texture analysis

This chapter presents a novel and generic framework for image segmentation using a compound image descriptor that encompasses both colour and texture information in an adaptive fashion. The developed image segmentation method extracts the texture information using low-level image descriptors (such as the Local Binary Patterns (LBP)) and colour information by using colour space partitioning. The main advantage of this approach is the analysis of the textured images at a micro-level using the local distribution of the LBP values, and in the colour domain by analysing the local colour distribution obtained after colour segmentation. The use of the colour and texture information separately has proven to be inappropriate for natural images as they are generally heterogeneous with respect to colour and texture characteristics. Thus, the main problem is to use the colour and texture information in a joint descriptor that can adapt to the local properties of the image under analysis. We will review existing approaches to colour and texture analysis as well as illustrating how our approach can be successfully applied to a range of applications including the segmentation of natural images, medical imaging and product inspection

Differential digital holography in quality control

U radu se opisuje metoda diferencijalne digitalne holografije koju je moguće primijeniti u kontroli kvalitete u nekim proizvodnim procesima. Predložena metoda može razlikovati 3D objekte bez oštećenja i iste objekte ali s nekim oštećenjem koje se pojavilo tijekom proizvodnje. Snimaju se hologrami svih objekata na vanosnom laboratorijskom postavu. Računalo se koristi za usporedbu holograma ili razlike holograma i određivanje ima li proizvedeni objekt neko oštećenje ili grešku. Pogreške mogu biti u vidu pogrešaka u teksturi na površini proizvoda ili u obliku nepravilnog reljefa proizvoda. Predložen je model postava za proizvodnju u industrijskim procesima.This paper describes a method of differential digital holography applicable in quality control for some manufacturing processes. The proposed method is able to differentiate 3D objects without production defects and similar 3D objects with production defects. Holograms of all objects are created using off-axis laboratory setup. Computer is used to compare holograms or difference holograms and determine whether the manufactured object has some defect or no defect. Production defects can be in the form of pattern errors or in the form of surface bumps. A model of production setup for implementation in manufacturing processes is proposed