A Novel Strategy for Improving Robustness in Computer Vision Manufacturing Defect Detection

• URL: http://arxiv.org/abs/2305.09407v1
• Date: Tue, 16 May 2023 12:51:51 GMT
• Title: A Novel Strategy for Improving Robustness in Computer Vision Manufacturing Defect Detection
• Authors: Ahmad Mohamad Mezher and Andrew E. Marble
• Abstract summary: Visual quality inspection in high performance manufacturing can benefit from automation, due to cost savings and improved rigor. Deep learning techniques are the current state of the art for generic computer vision tasks like classification and object detection. Manufacturing data can pose a challenge for deep learning because data is highly repetitive and there are few images of defects or deviations to learn from.
• Score: 1.3198689566654107
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual quality inspection in high performance manufacturing can benefit from automation, due to cost savings and improved rigor. Deep learning techniques are the current state of the art for generic computer vision tasks like classification and object detection. Manufacturing data can pose a challenge for deep learning because data is highly repetitive and there are few images of defects or deviations to learn from. Deep learning models trained with such data can be fragile and sensitive to context, and can under-detect new defects not found in the training data. In this work, we explore training defect detection models to learn specific defects out of context, so that they are more likely to be detected in new situations. We demonstrate how models trained on diverse images containing a common defect type can pick defects out in new circumstances. Such generic models could be more robust to new defects not found data collected for training, and can reduce data collection impediments to implementing visual inspection on production lines. Additionally, we demonstrate that object detection models trained to predict a label and bounding box outperform classifiers that predict a label only on held out test data typical of manufacturing inspection tasks. Finally, we studied the factors that affect generalization in order to train models that work under a wider range of conditions.

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)
• An Evaluation of Continual Learning for Advanced Node Semiconductor Defect Inspection [0.11184789007828977]
  This work introduces a task-agnostic, meta-learning approach to semiconductor defect inspection. It enables the incremental addition of new defect classes and scales to create a more robust and generalized model. We have benchmarked our approach using real resist-wafer SEM (Scanning Electron Microscopy) datasets for two process steps, ADI and AEI.
  arXiv  Detail & Related papers  (2024-07-17T16:41:22Z)
• Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing [5.012204041812572]
  This paper develops a novel pseudo replay-based continual learning framework. It integrates class incremental learning and oversampling-based data generation. The effectiveness of the proposed framework is validated in three cases studies.
  arXiv  Detail & Related papers  (2023-12-05T04:43:23Z)
• Open-Vocabulary Video Anomaly Detection [57.552523669351636]
  Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal. Recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to detect unseen anomalies given seen anomalies and normal videos. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies.
  arXiv  Detail & Related papers  (2023-11-13T02:54:17Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• 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)
• Generative Modeling Helps Weak Supervision (and Vice Versa) [87.62271390571837]
  We propose a model fusing weak supervision and generative adversarial networks. It captures discrete variables in the data alongside the weak supervision derived label estimate. It is the first approach to enable data augmentation through weakly supervised synthetic images and pseudolabels.
  arXiv  Detail & Related papers  (2022-03-22T20:24:21Z)
• Improving generalization with synthetic training data for deep learning based quality inspection [0.0]
  supervised deep learning requires a large amount of annotated images for training. In practice, collecting and annotating such data is costly and laborious. We show the use of randomly generated synthetic training images can help tackle domain instability.
  arXiv  Detail & Related papers  (2022-02-25T16:51:01Z)
• Synthetic training data generation for deep learning based quality inspection [0.0]
  We present a generic simulation pipeline to render images of defective or healthy (non defective) parts. We assess the quality of the generated images by training deep learning networks and by testing them on real data from a manufacturer.
  arXiv  Detail & Related papers  (2021-04-07T08:07:57Z)
• Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles [69.9674326582747]
  This paper presents a visual framework to investigate neural network models subjected to adversarial examples. We show how observing these elements can quickly pinpoint exploited areas in a model.
  arXiv  Detail & Related papers  (2021-03-18T13:04:21Z)
• Understanding Classifier Mistakes with Generative Models [88.20470690631372]
  Deep neural networks are effective on supervised learning tasks, but have been shown to be brittle. In this paper, we leverage generative models to identify and characterize instances where classifiers fail to generalize. Our approach is agnostic to class labels from the training set which makes it applicable to models trained in a semi-supervised way.
  arXiv  Detail & Related papers  (2020-10-05T22:13:21Z)

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.