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

Abstract

In this paper, we systematically review recent advances in s urface inspection using computer vision and image processing techniques, particularly those based on t exture 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 acquisiti on are out of the scope of this survey. The surface defects are loosely separated into two types. On e is local textural irregularities which is the main concern for most visual surface inspection applicatio ns. The other is global deviation of colour and/or texture, where local pattern or texture does not exhibit abn ormalities. We refer this type of defects as shade or tonality problem. The second type of defects have been lar gely neglected until recently, particularly when colour imaging system has been widely used in visual inspect ion and where chromatic consistency plays an important role in quality control. The emphasis of this surv ey though is still on detecting local abnormalities, given the fact that majority of the reported works are dealin g with the first type of defects. The techniques used to inspect textural abnormalities are d iscussed in four categories, statistical ap- proaches, 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 deal ing with colour texture analysis are discussed separately. It is also worth noting that processing vector- valued data has its unique challenges, which con- ventional surface inspection methods have often ignored or do not encounter. We also compare classification approaches with novelty dete ction 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 o nly carried out on defect-free samples. How- ever, novelty detection is relatively easier to adapt and is particularly desirable when training samples are incomplete.