Pseudo-Supervised Defect Detection Using Robust Deep Convolutional Autoencoders
Robust Autoencoders separate the input image into a Signal(L) and a Noise(S) part which, intuitively speaking, roughly corresponds to a more stable background scene (L) and an undesired anomaly (or defect) (S). This property of the method provides a convenient theoretical basis for divorcing intermi...
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| Veröffentlicht in: | Sakarya university journal of computer and information sciences Jg. 5; H. 3; S. 385 - 403 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Sakarya University
31.12.2022
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| ISSN: | 2636-8129, 2636-8129 |
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| Abstract | Robust Autoencoders separate the input image into a Signal(L) and a Noise(S) part which, intuitively speaking, roughly corresponds to a more stable background scene (L) and an undesired anomaly (or defect) (S). This property of the method provides a convenient theoretical basis for divorcing intermittent anomalies that happen to clutter a relatively consistent background image. In this paper, we illustrate the use of Robust Deep Convolutional Autoencoders (RDCAE) for defect detection, via a pseudo-supervised training process. Our method introduces synthetic simulated defects (or structured noise) to the training process, that alleviates the scarcity of true (real-life) anomalous samples. As such, we offer a pseudo-supervised training process to devise a well-defined mechanism for deciding that the defect-normal discrimination capability of the autoencoders has reached to an acceptable point at training time. The experiment results illustrate that pseudo supervised Robust Deep Convolutional Autoencoders are very effective in identifying surface defects in an efficient way, compared to state of the art anomaly detection methods. |
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| AbstractList | Robust Autoencoders separate the input image into a Signal(L) and a Noise(S) part which, intuitively speaking, roughly corresponds to a more stable background scene (L) and an undesired anomaly (or defect) (S). This property of the method provides a convenient theoretical basis for divorcing intermittent anomalies that happen to clutter a relatively consistent background image. In this paper, we illustrate the use of Robust Deep Convolutional Autoencoders (RDCAE) for defect detection, via a pseudo-supervised training process. Our method introduces synthetic simulated defects (or structured noise) to the training process, that alleviates the scarcity of true (real-life) anomalous samples. As such, we offer a pseudo-supervised training process to devise a well-defined mechanism for deciding that the defect-normal discrimination capability of the autoencoders has reached to an acceptable point at training time. The experiment results illustrate that pseudo supervised Robust Deep Convolutional Autoencoders are very effective in identifying surface defects in an efficient way, compared to state of the art anomaly detection methods. |
| Author | Alpdemir, Mahmut Nedim |
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| Cites_doi | 10.1109/5.726791 10.1145/3097983.3098052 10.1145/1390156.1390294 10.13074/jent.2017.06.172255 10.1007/978-3-642-15883-4_27 10.1016/j.compind.2021.103459 10.1016/j.ijleo.2016.09.110 10.1145/2133360.2133363 10.1007/978-3-642-61717-1_13 10.1145/1541880.1541882 10.1145/3394486.3406704 10.1162/089976601750264965 10.1109/ACCESS.2020.3010274 10.1109/TKDE.2019.2947676 10.7717/peerj.453 10.1109/CVPR.2016.90 10.1007/978-3-030-32251-9_32 10.1016/j.patrec.2017.07.016 10.1016/j.sigpro.2013.12.026 10.1109/DSW.2019.8755576 10.1364/OE.27.013263 10.1023/B:AIRE.0000045502.10941.a9 10.1016/j.imavis.2011.02.002 10.1109/ICCV.2001.937541 10.3390/s20051459 10.1109/TIP.2003.819861 10.1038/nature14539 10.2478/aut-2019-0035 10.1109/ICCV.2019.00179 10.1145/1970392.1970395 10.1007/978-3-319-71249-9_3 10.1023/B:MACH.0000008084.60811.49 10.1117/12.2518459 10.1214/aoms/1177704472 10.1109/ICPR48806.2021.9412842 10.1155/2021/9948808 |
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| SubjectTerms | anomaly detection convolutional neural networks defect detection machine learning robust autoencoders |
| Title | Pseudo-Supervised Defect Detection Using Robust Deep Convolutional Autoencoders |
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