Abstract
In this paper, we propose a Phase Fourier Reconstruction (PFR) approach for anomaly detection on metal surfaces using salient irregularities. To get salient irregularity with images captured from an automatic visual inspection (AVI) system using different lighting settings, we first trained a classifier for image selection as only dark images are utilized for anomaly detection. By doing so, surface details, part design, and boundaries between foreground/background become indistinct, but anomaly regions are highlighted because of diffuse reflection caused by rough surfaces. Then PFR is applied so that regular patterns and homogeneous regions are further de-emphasized, and simultaneously, anomaly areas are distinct and located. Different from existing phase-based methods which require substantial texture information, our PFR works on both textual and non-textual images. Unlike existing template matching methods which require prior knowledge of defect-free patterns, our PFR is an unsupervised approach which detects anomalies using a single image. Experimental results on anomaly detection clearly demonstrate the effectiveness of the proposed method which outperforms several well-designed methods [8, 12, 15, 16, 18, 19] with a running time of less than 0.01 seconds per image.
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Acknowledgement
This work was conducted within Rolls-Royce@NTU Corporate Lab with the support of National Research Foundation under the CorpLab@University scheme.
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Hung, TY., Vaikundam, S., Natarajan, V., Chia, LT. (2017). Phase Fourier Reconstruction for Anomaly Detection on Metal Surface Using Salient Irregularity. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10132. Springer, Cham. https://doi.org/10.1007/978-3-319-51811-4_24
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DOI: https://doi.org/10.1007/978-3-319-51811-4_24
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