309 research outputs found
Manufacturing Quality Control with Autoencoder-Based Defect Localization and Unsupervised Class Selection
Manufacturing industries require efficient and voluminous production of
high-quality finished goods. In the context of Industry 4.0, visual anomaly
detection poses an optimistic solution for automatically controlling product
quality with high precision. Automation based on computer vision poses a
promising solution to prevent bottlenecks at the product quality checkpoint. We
considered recent advancements in machine learning to improve visual defect
localization, but challenges persist in obtaining a balanced feature set and
database of the wide variety of defects occurring in the production line. This
paper proposes a defect localizing autoencoder with unsupervised class
selection by clustering with k-means the features extracted from a pre-trained
VGG-16 network. The selected classes of defects are augmented with natural wild
textures to simulate artificial defects. The study demonstrates the
effectiveness of the defect localizing autoencoder with unsupervised class
selection for improving defect detection in manufacturing industries. The
proposed methodology shows promising results with precise and accurate
localization of quality defects on melamine-faced boards for the furniture
industry. Incorporating artificial defects into the training data shows
significant potential for practical implementation in real-world quality
control scenarios
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images
In line with the development of Industry 4.0, surface defect
detection/anomaly detection becomes a topical subject in the industry field.
Improving efficiency as well as saving labor costs has steadily become a matter
of great concern in practice, where deep learning-based algorithms perform
better than traditional vision inspection methods in recent years. While
existing deep learning-based algorithms are biased towards supervised learning,
which not only necessitates a huge amount of labeled data and human labor, but
also brings about inefficiency and limitations. In contrast, recent research
shows that unsupervised learning has great potential in tackling the above
disadvantages for visual industrial anomaly detection. In this survey, we
summarize current challenges and provide a thorough overview of recently
proposed unsupervised algorithms for visual industrial anomaly detection
covering five categories, whose innovation points and frameworks are described
in detail. Meanwhile, publicly available datasets for industrial anomaly
detection are introduced. By comparing different classes of methods, the
advantages and disadvantages of anomaly detection algorithms are summarized.
Based on the current research framework, we point out the core issue that
remains to be resolved and provide further improvement directions. Meanwhile,
based on the latest technological trends, we offer insights into future
research directions. It is expected to assist both the research community and
industry in developing a broader and cross-domain perspective
Anomaly Detection in Automated Fibre Placement: Learning with Data Limitations
Conventional defect detection systems in Automated Fibre Placement (AFP)
typically rely on end-to-end supervised learning, necessitating a substantial
number of labelled defective samples for effective training. However, the
scarcity of such labelled data poses a challenge. To overcome this limitation,
we present a comprehensive framework for defect detection and localization in
Automated Fibre Placement. Our approach combines unsupervised deep learning and
classical computer vision algorithms, eliminating the need for labelled data or
manufacturing defect samples. It efficiently detects various surface issues
while requiring fewer images of composite parts for training. Our framework
employs an innovative sample extraction method leveraging AFP's inherent
symmetry to expand the dataset. By inputting a depth map of the fibre layup
surface, we extract local samples aligned with each composite strip (tow).
These samples are processed through an autoencoder, trained on normal samples
for precise reconstructions, highlighting anomalies through reconstruction
errors. Aggregated values form an anomaly map for insightful visualization. The
framework employs blob detection on this map to locate manufacturing defects.
The experimental findings reveal that despite training the autoencoder with a
limited number of images, our proposed method exhibits satisfactory detection
accuracy and accurately identifies defect locations. Our framework demonstrates
comparable performance to existing methods, while also offering the advantage
of detecting all types of anomalies without relying on an extensive labelled
dataset of defects
Self-supervised pre-training of CNNs for flatness defect classification in the steelworks industry
Classification of surface defects in the steelworks industry plays a significant role in guaranteeing the quality of the products. From an industrial point of view, a serious concern is represented by the hot-rolled products shape defects and particularly those concerning the strip flatness. Flatness defects are typically divided into four sub-classes depending on which part of the strip is affected and the corresponding shape. In the context of this research, the primary objective is evaluating the improvements of exploiting the self-supervised learning paradigm for defects classification, taking advantage of unlabelled, real, steel strip flatness maps. Different pre-training methods are compared, as well as architectures, taking advantage of well-established neural subnetworks, such as Residual and Inception modules. A systematic approach in evaluating the different performances guarantees a formal verification of the self-supervised pre-training paradigms evaluated hereafter. In particular, pre-training neural networks with the EgoMotion meta-algorithm shows classification improvements over the AutoEncoder technique, which in turn is better performing than a Glorot weight initialization
A survey on generative adversarial networks for imbalance problems in computer vision tasks
Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Neural Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets. In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms
Exploring the Relationship between Samples and Masks for Robust Defect Localization
Defect detection aims to detect and localize regions out of the normal
distribution.Previous approaches model normality and compare it with the input
to identify defective regions, potentially limiting their generalizability.This
paper proposes a one-stage framework that detects defective patterns directly
without the modeling process.This ability is adopted through the joint efforts
of three parties: a generative adversarial network (GAN), a newly proposed
scaled pattern loss, and a dynamic masked cycle-consistent auxiliary network.
Explicit information that could indicate the position of defects is
intentionally excluded to avoid learning any direct mapping.Experimental
results on the texture class of the challenging MVTec AD dataset show that the
proposed method is 2.9% higher than the SOTA methods in F1-Score, while
substantially outperforming SOTA methods in generalizability
Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review
A growing need exists for efficient and accurate methods for detecting
defects in semiconductor materials and devices. These defects can have a
detrimental impact on the efficiency of the manufacturing process, because they
cause critical failures and wafer-yield limitations. As nodes and patterns get
smaller, even high-resolution imaging techniques such as Scanning Electron
Microscopy (SEM) produce noisy images due to operating close to sensitivity
levels and due to varying physical properties of different underlayers or
resist materials. This inherent noise is one of the main challenges for defect
inspection. One promising approach is the use of machine learning algorithms,
which can be trained to accurately classify and locate defects in semiconductor
samples. Recently, convolutional neural networks have proved to be particularly
useful in this regard. This systematic review provides a comprehensive overview
of the state of automated semiconductor defect inspection on SEM images,
including the most recent innovations and developments. 38 publications were
selected on this topic, indexed in IEEE Xplore and SPIE databases. For each of
these, the application, methodology, dataset, results, limitations and future
work were summarized. A comprehensive overview and analysis of their methods is
provided. Finally, promising avenues for future work in the field of SEM-based
defect inspection are suggested.Comment: 16 pages, 12 figures, 3 table
Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization
In the machine learning domain, research on anomaly detection and
localization within image data has garnered significant attention, particularly
in practical applications such as industrial defect detection. While existing
approaches predominantly rely on Convolutional Neural Networks (CNN) as their
backbone network, we propose an innovative method based on the Transformer
backbone network. Our approach employs a two-stage incremental learning
strategy. In the first stage, we train a Masked Autoencoder (MAE) model
exclusively on normal images. Subsequently, in the second stage, we implement
pixel-level data augmentation techniques to generate corrupted normal images
and their corresponding pixel labels. This process enables the model to learn
how to repair corrupted regions and classify the state of each pixel.
Ultimately, the model produces a pixel reconstruction error matrix and a pixel
anomaly probability matrix, which are combined to create an anomaly scoring
matrix that effectively identifies abnormal regions. When compared to several
state-of-the-art CNN-based techniques, our method demonstrates superior
performance on the MVTec AD dataset, achieving an impressive 97.6% AUC
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