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Proceedings Paper

On selecting reference image models for anomaly detection in industrial systems
Author(s): Xinhua Xiao; Jin Quan; Andrew Ferro; Chia Y. Han; Xuefu Zhou; William G. Wee
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Paper Abstract

Automatic X-ray inspection of industrial parts usually uses reference-based methods, in which a set of model images or statistics extracted from the model image set are selected as the benchmark. Based on these methods, many systems are developed and are used extensively for anomaly detection. However, the performance of these systems relies heavily on the model image set. Thus, the selection of the model images is very important. This paper presents an approach for automatically selecting a set of model images to be used in a reference-based assisted defect recognition (ADR) system for anomaly detection of turbine blades of jet engines. The proposed approach to generating a model image set is based on feature extraction. Features are extracted from callout images of ADR, including potential defect indication type, size and location. Experimental results show that the proposed approach is fast and a low false alarm rate with acceptable detection rate is ensured. Moreover, the approach is applicable to different blade types and varied views of the blade. Further validation shows that the approach can be applied to the update of the model image set, when more images are generated from new blades and the model becomes inaccurate for anomaly detection in the new images.

Paper Details

Date Published: 26 September 2013
PDF: 9 pages
Proc. SPIE 8856, Applications of Digital Image Processing XXXVI, 88560O (26 September 2013); doi: 10.1117/12.2023471
Show Author Affiliations
Xinhua Xiao, Univ. of Cincinnati (United States)
Jin Quan, Univ. of Cincinnati (United States)
Andrew Ferro, GE Aviation (United States)
Chia Y. Han, Univ. of Cincinnati (United States)
Xuefu Zhou, Univ. of Cincinnati (United States)
William G. Wee, Univ. of Cincinnati (United States)


Published in SPIE Proceedings Vol. 8856:
Applications of Digital Image Processing XXXVI
Andrew G. Tescher, Editor(s)

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