TransferD2: Automated Defect Detection Approach in Smart Manufacturing
using Transfer Learning Techniques
- URL: http://arxiv.org/abs/2302.13317v1
- Date: Sun, 26 Feb 2023 13:24:46 GMT
- Title: TransferD2: Automated Defect Detection Approach in Smart Manufacturing
using Transfer Learning Techniques
- Authors: Atah Nuh Mih, Hung Cao, Joshua Pickard, Monica Wachowicz, Rickey Dubay
- Abstract summary: We propose a transfer learning approach, namely TransferD2, to correctly identify defects on a dataset of source objects.
Our proposed approach can be applied in defect detection applications where insufficient data is available for training a model and can be extended to identify imperfections in new unseen data.
- Score: 1.8899300124593645
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Quality assurance is crucial in the smart manufacturing industry as it
identifies the presence of defects in finished products before they are shipped
out. Modern machine learning techniques can be leveraged to provide rapid and
accurate detection of these imperfections. We, therefore, propose a transfer
learning approach, namely TransferD2, to correctly identify defects on a
dataset of source objects and extend its application to new unseen target
objects. We present a data enhancement technique to generate a large dataset
from the small source dataset for building a classifier. We then integrate
three different pre-trained models (Xception, ResNet101V2, and
InceptionResNetV2) into the classifier network and compare their performance on
source and target data. We use the classifier to detect the presence of
imperfections on the unseen target data using pseudo-bounding boxes. Our
results show that ResNet101V2 performs best on the source data with an accuracy
of 95.72%. Xception performs best on the target data with an accuracy of 91.00%
and also provides a more accurate prediction of the defects on the target
images. Throughout the experiment, the results also indicate that the choice of
a pre-trained model is not dependent on the depth of the network. Our proposed
approach can be applied in defect detection applications where insufficient
data is available for training a model and can be extended to identify
imperfections in new unseen data.
Related papers
- DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
Current perceptive models heavily depend on resource-intensive datasets.
We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability.
Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
arXiv Detail & Related papers (2024-03-20T04:58:03Z) - 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) - DiffusionEngine: Diffusion Model is Scalable Data Engine for Object
Detection [41.436817746749384]
Diffusion Model is a scalable data engine for object detection.
DiffusionEngine (DE) provides high-quality detection-oriented training pairs in a single stage.
arXiv Detail & Related papers (2023-09-07T17:55:01Z) - Building Manufacturing Deep Learning Models with Minimal and Imbalanced
Training Data Using Domain Adaptation and Data Augmentation [15.333573151694576]
We propose a novel domain adaptation (DA) approach to address the problem of labeled training data scarcity for a target learning task.
Our approach works for scenarios where the source dataset and the dataset available for the target learning task have same or different feature spaces.
We evaluate our combined approach using image data for wafer defect prediction.
arXiv Detail & Related papers (2023-05-31T21:45:34Z) - Combating noisy labels in object detection datasets [0.0]
We introduce the Confident Learning for Object Detection (CLOD) algorithm for assessing the quality of each label in object detection datasets.
We identify missing, spurious, mislabeled, and mislocated bounding boxes and suggesting corrections.
The proposed method is able to point out nearly 80% of artificially disturbed bounding boxes with a false positive rate below 0.1.
arXiv Detail & Related papers (2022-11-25T10:05:06Z) - Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions.
In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data.
We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
arXiv Detail & Related papers (2022-01-11T23:01:12Z) - Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D
Object Detection [85.11649974840758]
3D object detection networks tend to be biased towards the data they are trained on.
We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
arXiv Detail & Related papers (2021-11-30T18:42:42Z) - Improving Uncertainty Calibration via Prior Augmented Data [56.88185136509654]
Neural networks have proven successful at learning from complex data distributions by acting as universal function approximators.
They are often overconfident in their predictions, which leads to inaccurate and miscalibrated probabilistic predictions.
We propose a solution by seeking out regions of feature space where the model is unjustifiably overconfident, and conditionally raising the entropy of those predictions towards that of the prior distribution of the labels.
arXiv Detail & Related papers (2021-02-22T07:02:37Z) - Negative Data Augmentation [127.28042046152954]
We show that negative data augmentation samples provide information on the support of the data distribution.
We introduce a new GAN training objective where we use NDA as an additional source of synthetic data for the discriminator.
Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
arXiv Detail & Related papers (2021-02-09T20:28:35Z) - FaultFace: Deep Convolutional Generative Adversarial Network (DCGAN)
based Ball-Bearing Failure Detection Method [4.543665832042712]
This paper proposes a methodology called FaultFace for failure detection on Ball-Bearing joints for rotational shafts.
Deep Convolutional Generative Adversarial Network is employed to produce new faceportraits of the nominal and failure behaviors to get a balanced dataset.
arXiv Detail & Related papers (2020-07-30T06:37:53Z)
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.