A sight on defect detection methods for imbalanced industrial data

Chaabi, Meryem; Hamlich, Mohamed

doi:10.1051/itmconf/20224301012

Cited by 3 publications

(1 citation statement)

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“…The term OCC was first introduced by [9] to denote a category of classification algorithms that address cases where few to none defect samples are available for training; the normal class is well-defined while abnormal one is under-sampled [10] which is quite common in industrial areas [11] ,and with that, defects are seen as a deviation from defect-free class. The OCC concept encompasses several approaches, such as methods based on density [12], distance [13], neural networks [14], [15], and boundary approaches [16] that aims to encircle normal samples by a decision boundary.…”

Section: Introductionmentioning

confidence: 99%

Product defect detection based on convolutional autoencoder and one-class classification

Chaabi¹,

Hamlich²,

Garouani³

2023

IJ-AI

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<span lang="EN-US">To meet customer expectations and remain competitive, industrials try constantly to improve their quality control systems. There is hence increasing demand for adopting automatic defect detection solutions. However, the biggest issue in addressing such systems is the imbalanced aspect of industrial datasets. Often, defect-free samples far exceed the defected ones, due to continuous improvement approaches adopted by manufacturing companies. In this sense, we propose an automatic defect detection system based on one-class classification (OCC) since it involves only normal samples during training. It consists of three sub-models, first, a convolutional autoencoder serves as latent features extractor, the extracted features vectors are subsequently fed into the dimensionality reduction process by performing principal component analysis (PCA), then the reduced-dimensional data are used to train the one-class classifier support vector data description (SVDD). During the test phase, both normal and defected images are used. The first two stages of the trained model generate a low-dimensional features vector, whereas the SVDD classifies the new input, whether it is defect-free or defected. This approach is evaluated on the carpet images from the industrial inspection dataset MVTec anomaly detection (MVTec AD). During training, only normal images were used. The results showed that the proposed method outperforms the state-of-the-art methods.</span>

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