A sparse representation based fast detection method for surface defect detection of bottle caps


Autoria(s): Zhou, Wenju; Fei, Minrui; Zhou, Huiyu; Li, Kang
Data(s)

10/01/2014

Resumo

A practical machine-vision-based system is developed for fast detection of defects occurring on the surface of bottle caps. This system can be used to extract the circular region as the region of interests (ROI) from the surface of a bottle cap, and then use the circular region projection histogram (CRPH) as the matching features. We establish two dictionaries for the template and possible defect, respectively. Due to the requirements of high-speed production as well as detecting quality, a fast algorithm based on a sparse representation is proposed to speed up the searching. In the sparse representation, non-zero elements in the sparse factors indicate the defect's size and position. Experimental results in industrial trials show that the proposed method outperforms the orientation code method (OCM) and is able to produce promising results for detecting defects on the surface of bottle caps.

Identificador

http://pure.qub.ac.uk/portal/en/publications/a-sparse-representation-based-fast-detection-method-for-surface-defect-detection-of-bottle-caps(2d306f26-20aa-4261-96ea-3e2c50d1a34e).html

http://dx.doi.org/10.1016/j.neucom.2013.07.038

Idioma(s)

eng

Direitos

info:eu-repo/semantics/restrictedAccess

Fonte

Zhou , W , Fei , M , Zhou , H & Li , K 2014 , ' A sparse representation based fast detection method for surface defect detection of bottle caps ' Neurocomputing , vol 123 , pp. 406-414 . DOI: 10.1016/j.neucom.2013.07.038

Palavras-Chave #/dk/atira/pure/subjectarea/asjc/1700/1702 #Artificial Intelligence #/dk/atira/pure/subjectarea/asjc/1700/1706 #Computer Science Applications #/dk/atira/pure/subjectarea/asjc/2800/2805 #Cognitive Neuroscience
Tipo

article