Paper
26 September 2013 On selecting reference image models for anomaly detection in industrial systems
Xinhua Xiao, Jin Quan, Andrew Ferro, Chia Y. Han, Xuefu Zhou, William G. Wee
Author Affiliations +
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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xinhua Xiao, Jin Quan, Andrew Ferro, Chia Y. Han, Xuefu Zhou, and William G. Wee "On selecting reference image models for anomaly detection in industrial systems", Proc. SPIE 8856, Applications of Digital Image Processing XXXVI, 88560O (26 September 2013); https://doi.org/10.1117/12.2023471
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Cited by 1 scholarly publication.
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KEYWORDS
Statistical modeling

Performance modeling

Systems modeling

Feature extraction

Statistical analysis

Inspection

X-ray imaging

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