Exploiting sparsity for image-based object surface anomaly detection

The anomaly detection task plays an important role in quality control in many industrial or manufacturing processes. However, in many such processes, anomaly detection is done visually by human experts who have in-depth knowledge and vast experience on a product in order to perform well in the detec...

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Bibliographic Details
Main Authors: Chai, Woon Huei, Ho, Shen-Shyang, Goh, Chi-Keong
Format: Conference Proceeding
Language:English
Subjects:
Anomalies
anomaly detection
defect detection
Dictionaries
Electronics
Human performance
Image classification
Image detection
Image reconstruction
Inspection
Metals
Optimization
Parameter optimization
Quality control
quality control and automated inspection
sparse reconstruction
Surface defects
Tasks
Training
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Description
Summary:The anomaly detection task plays an important role in quality control in many industrial or manufacturing processes. However, in many such processes, anomaly detection is done visually by human experts who have in-depth knowledge and vast experience on a product in order to perform well in the detection task. In this paper, we present an approach that (i) identifies anomalies in an image based on the sparse residuals (or errors) obtained during image reconstruction using sparse representation and (ii) learns the threshold to classify an image pixel based on its residual value. The intuitions for our proposed sparse approximation driven approach are, namely: (i) anomalies are infrequent and (ii) anomalies are unwanted portions of an image reconstruction. Empirical results on a real-world image dataset for an industrial surface defect detection task are used to demonstrate the feasibility of our proposed approach.
ISSN:2379-190X
DOI:10.1109/ICASSP.2016.7472024