Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks

• URL: http://arxiv.org/abs/2011.04121v2
• Date: Tue, 10 Nov 2020 04:20:49 GMT
• Title: Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks
• Authors: Tareq Tayeh, Sulaiman Aburakhia, Ryan Myers, and Abdallah Shami
• Abstract summary: Surface anomaly detection plays an important quality control role in many manufacturing industries to reduce scrap production. Deep learning Convolutional Neural Networks (CNNs) have been at the forefront of these image processing-based solutions. In this paper, we address that challenge by training the CNN on surface texture patches with a distance-based anomaly detection objective.
• Score: 2.7173993697663086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surface anomaly detection plays an important quality control role in many manufacturing industries to reduce scrap production. Machine-based visual inspections have been utilized in recent years to conduct this task instead of human experts. In particular, deep learning Convolutional Neural Networks (CNNs) have been at the forefront of these image processing-based solutions due to their predictive accuracy and efficiency. Training a CNN on a classification objective requires a sufficiently large amount of defective data, which is often not available. In this paper, we address that challenge by training the CNN on surface texture patches with a distance-based anomaly detection objective instead. A deep residual-based triplet network model is utilized, and defective training samples are synthesized exclusively from non-defective samples via random erasing techniques to directly learn a similarity metric between the same-class samples and out-of-class samples. Evaluation results demonstrate the approach's strength in detecting different types of anomalies, such as bent, broken, or cracked surfaces, for known surfaces that are part of the training data and unseen novel surfaces.

Related papers

• SINDER: Repairing the Singular Defects of DINOv2 [61.98878352956125]
  Vision Transformer models trained on large-scale datasets often exhibit artifacts in the patch token they extract. We propose a novel fine-tuning smooth regularization that rectifies structural deficiencies using only a small dataset.
  arXiv  Detail & Related papers  (2024-07-23T20:34:23Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• Unsupervised textile defect detection using convolutional neural networks [0.0]
  We propose a novel motif-based approach for unsupervised textile anomaly detection. It combines the benefits of traditional convolutional neural networks with those of an unsupervised learning paradigm. We demonstrate the effectiveness of our approach on the Patterned Fabrics benchmark dataset.
  arXiv  Detail & Related papers  (2023-11-30T22:08:06Z)
• Adaptive Face Recognition Using Adversarial Information Network [57.29464116557734]
  Face recognition models often degenerate when training data are different from testing data. We propose a novel adversarial information network (AIN) to address it.
  arXiv  Detail & Related papers  (2023-05-23T02:14:11Z)
• Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples [21.358496646676087]
  We design a teacher-student structured model for 3D anomaly detection. Specifically, we use feature space alignment, dimension zoom, and max pooling to extract the features of the point cloud. Our method only requires very few normal samples to train the student network.
  arXiv  Detail & Related papers  (2022-10-31T12:29:55Z)
• Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data [69.25738064847175]
  It is necessary to consider the behavior of the signals in each sensor separately, to take into account their correlation and hidden relationships with each other. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance.
  arXiv  Detail & Related papers  (2022-10-20T11:03:21Z)
• Few-shot incremental learning in the context of solar cell quality inspection [0.0]
  In this work, we have explored the technique of weight imprinting in the context of solar cell quality inspection. The results have shown that this technique allows the network to extend its knowledge with regard to defect classes with few samples.
  arXiv  Detail & Related papers  (2022-07-01T23:52:07Z)
• TL-SDD: A Transfer Learning-Based Method for Surface Defect Detection with Few Samples [17.884998028369026]
  We propose TL-SDD: a novel Transfer Learning-based method for Surface Defect Detection. We adopt a two-phase training scheme to transfer the knowledge from common defect classes to rare defect classes. Compared to the baseline methods, the performance of our proposed method has improved by up to 11.98% for rare defect classes.
  arXiv  Detail & Related papers  (2021-08-16T07:24:00Z)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.