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

By Xianghua Xie. 2008

559 citations recorded by the source corpus.

In this paper, we systematically review recent advances in surface inspection using computer vision and image processing techniques, particularly those based on texture analysis methods. The aim is to review the state-of-the-art techniques for the purposes of visual inspection and decision making schemes that are able to discriminate the features extracted from normal and defective regions. This field is so vast that it is impossible to cover all the aspects of visual inspection. This paper focuses on a particular but important subset which generally treats visual surface inspection as texture analysis problems. Other topics related to visual 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 the main concern for most visual surface inspection applications. The other is global deviation of colour and/or texture, where local pattern or texture does not exhibit abnormalities. We refer this type of defects as shade or tonality problem. The second type of defects have been largely neglected until recently, particularly when colour imaging system has been widely used in visual inspection and where chromatic consistency plays an important 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 comprehensive list of references to some recent works. Due to rising demand and practice of colour texture analysis in application to visual inspection, those works that are dealing with colour texture analysis are discussed separately. It is also worth noting that processing vector-valued data has its unique challenges, which conventional surface inspection methods have often ignored or do not encounter. We also compare classification approaches with novelty detection approaches at the decision making stage. Classification approaches often require supervised training and usually provide better performance than 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 are incomplete

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/f7b41cc0e1972d914e3f2cea29372555fb228453

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Citations in the corpus

Review of vision-based steel surface inspection systems (2014, methodology): “Over the years, a number of review papers [6-12] on various aspects of surface defect detection have been reported.”
Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY (2020, methodology): “However, several studies have widely demonstrated that these are highly accurate techniques [2].”
Wavelet-based multiscale texture segmentation: Application to stromal compartment characterization on virtual slides (2010, methodology): “…of methods may be distinguished : the statistical methods which exploit the spatial distribution of the gray levels, the methods based on the construction of a texture model, and the methods which mimic the mechanism of the human vision, integrating several levels of resolution (Xie, 2008, e.g.).”
Automated Visual Defect Detection for Flat Steel Surface: A Survey (2020, background): “A number of AVI surveys (such as [11-13]) with a wide coverage of inspection problems can be available successively.”
Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection (2018, methodology): “These insights inspired the use of multi-scale techniques for damage detection purposes.(10) Furuta et al.”
Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection (2016, background): “However, required to ensure stable and reliable defect detection results described by Xie [1] and Neogi et al. [2], existing methods roughly be divided into four main categories: statistical, structu filter based, and model based.”
Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges (2020, unknown): “For surface defect detection of manufactured products, Xianghua Xie [12] systematically recent advances in surface detection using computer vision and image processing techniques.”
A Generic Deep-Learning-Based Approach for Automated Surface Inspection (2018, methodology): “Depending on the properties of surface texture, ASI methods can be divided into four categories [6]: 1) structural; 2) statistical; 3) ﬁlter-based; and 4) model-based approaches.”