DFDA-AD: An Approach with Dual Feature Extraction Architecture and Dual Attention Mechanism for Image Anomaly Detection
Detecting and locating unwanted structures or anomalies in the image is one of the important issues in machine vision and industrial inspection. The complexity and variability of data distribution and the lack of labeled data are among the challenges of detecting anomalies in images. In recent years...
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Semnan University
2024-12-01
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| Series: | مجله مدل سازی در مهندسی |
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| Online Access: | https://modelling.semnan.ac.ir/article_9172_5e0763eb489e8328b2d7bd4639777686.pdf |
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| author | Babak Masoudi |
| author_facet | Babak Masoudi |
| author_sort | Babak Masoudi |
| collection | DOAJ |
| description | Detecting and locating unwanted structures or anomalies in the image is one of the important issues in machine vision and industrial inspection. The complexity and variability of data distribution and the lack of labeled data are among the challenges of detecting anomalies in images. In recent years, deep learning methods have provided promising results for solving anomaly detection problems in any data types, especially in images. In this paper, the DFDA-AD architecture, which is an unsupervised approach based on deep learning, is proposed for anomaly detection in industrial images. DFDA-AD consists of dual feature extraction from images by pre-trained DenseNet121 and ResNet50 networks. Two attention mechanisms are improved and developed in this paper, which provide more important feature maps for clustering by K-means algorithm. The evaluation of the model's performance was done on the MVTec AD data set, and the results of the evaluations for anomaly detection and localization were satisfactory compared to several other approaches that have been recently proposed. |
| format | Article |
| id | doaj-art-774804d9caf048279d26f9150c376882 |
| institution | Kabale University |
| issn | 2008-4854 2783-2538 |
| language | fas |
| publishDate | 2024-12-01 |
| publisher | Semnan University |
| record_format | Article |
| series | مجله مدل سازی در مهندسی |
| spelling | doaj-art-774804d9caf048279d26f9150c3768822025-01-15T08:17:35ZfasSemnan Universityمجله مدل سازی در مهندسی2008-48542783-25382024-12-012279455710.22075/jme.2024.31105.24849172DFDA-AD: An Approach with Dual Feature Extraction Architecture and Dual Attention Mechanism for Image Anomaly DetectionBabak Masoudi0Department of Information Technology, Payamenoor University (PNU), P.O.Box, 19395-3697 Tehran, I.R of IranDetecting and locating unwanted structures or anomalies in the image is one of the important issues in machine vision and industrial inspection. The complexity and variability of data distribution and the lack of labeled data are among the challenges of detecting anomalies in images. In recent years, deep learning methods have provided promising results for solving anomaly detection problems in any data types, especially in images. In this paper, the DFDA-AD architecture, which is an unsupervised approach based on deep learning, is proposed for anomaly detection in industrial images. DFDA-AD consists of dual feature extraction from images by pre-trained DenseNet121 and ResNet50 networks. Two attention mechanisms are improved and developed in this paper, which provide more important feature maps for clustering by K-means algorithm. The evaluation of the model's performance was done on the MVTec AD data set, and the results of the evaluations for anomaly detection and localization were satisfactory compared to several other approaches that have been recently proposed.https://modelling.semnan.ac.ir/article_9172_5e0763eb489e8328b2d7bd4639777686.pdfabnormality detectionattention mechanismdeep learningindustrial imagestransfer learning |
| spellingShingle | Babak Masoudi DFDA-AD: An Approach with Dual Feature Extraction Architecture and Dual Attention Mechanism for Image Anomaly Detection مجله مدل سازی در مهندسی abnormality detection attention mechanism deep learning industrial images transfer learning |
| title | DFDA-AD: An Approach with Dual Feature Extraction Architecture and Dual Attention Mechanism for Image Anomaly Detection |
| title_full | DFDA-AD: An Approach with Dual Feature Extraction Architecture and Dual Attention Mechanism for Image Anomaly Detection |
| title_fullStr | DFDA-AD: An Approach with Dual Feature Extraction Architecture and Dual Attention Mechanism for Image Anomaly Detection |
| title_full_unstemmed | DFDA-AD: An Approach with Dual Feature Extraction Architecture and Dual Attention Mechanism for Image Anomaly Detection |
| title_short | DFDA-AD: An Approach with Dual Feature Extraction Architecture and Dual Attention Mechanism for Image Anomaly Detection |
| title_sort | dfda ad an approach with dual feature extraction architecture and dual attention mechanism for image anomaly detection |
| topic | abnormality detection attention mechanism deep learning industrial images transfer learning |
| url | https://modelling.semnan.ac.ir/article_9172_5e0763eb489e8328b2d7bd4639777686.pdf |
| work_keys_str_mv | AT babakmasoudi dfdaadanapproachwithdualfeatureextractionarchitectureanddualattentionmechanismforimageanomalydetection |