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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王勝德 | zh_TW |
| dc.contributor.advisor | Sheng-De Wang | en |
| dc.contributor.author | 蔡旻均 | zh_TW |
| dc.contributor.author | Min-Chun Tsai | en |
| dc.date.accessioned | 2023-01-10T17:04:06Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-01-07 | - |
| dc.date.issued | 2022 | - |
| dc.date.submitted | 2002-01-01 | - |
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Steger, “Mvtec ad – a comprehensive real-world dataset for unsupervised anomaly detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019, pp. 9592-9600. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems, vol. 32, 2019. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83163 | - |
| dc.description.abstract | 在圖像異常檢測的領域，異常的部分通常是少見而且無法預測的。因此，我們的目標是構建一個檢測架構，能夠在只有正常數據的情況下檢測未知的異常。在本文中，我們介紹了一個兩階段架構，其使用自監督學習來檢測和定位圖像中的異常。我們利用設計的數據增強策略來模擬真實的異常，讓模型學習區分正常數據和合成的異常數據。此外，我們比較了兩種可以結合不同層級語意特徵的方法，這兩種方法在異常檢測上都獲得了不錯的結果。無需額外的訓練資料和預訓練模型，本文提出的方法在MVTec AD 基準數據集上達到了96.4% AUROC的異常檢測分數及96.1% AUROC的異常定位分數，足以和現有的論文方法競爭。此結果展現了我們的方法在產業應用中的潛力。 | zh_TW |
| dc.description.abstract | In visual anomaly detection, anomalies are often rare and unpredictable. For this reason, we aim to build a detection framework that can detect unseen anomalies with only anomaly-free examples. In this paper, we introduce a two-stage framework for detecting and localizing anomalies in images using self-supervised learning. We simulate anomalies through the designed augmentation strategies, and the model learns to distinguish normal data from synthetic anomalies. In addition, we compare two methods for combining representations from different semantic levels of our network, and both of the methods obtain competitive results for defect detection. Without extra training samples and pre-trained models, the proposed approach achieves 96.4% detection AUROC and 96.1% localization AUROC on the MVTec AD benchmark, which is competitive against existing unsupervised methods. The results demonstrate the potential of our method for industrial applications. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-01-10T17:04:06Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-01-10T17:04:06Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i 誌謝 ii 摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES vii LIST OF TABLES viii Chapter 1 Introduction 1 Chapter 2 Related Work 3 2.1 Reconstruction-based Methods 3 2.2 Self-supervised Learning 4 2.3 Transfer Learning 5 2.4 Discrete Wavelet Transform 6 Chapter 3 Approach 7 3.1 Synthetic Anomalies Generation 7 3.1.1 CutPaste 7 3.1.2 PerlinImgAug 8 3.2 Training 11 3.2.1 Multi-class Classification 11 3.3 Inference 12 3.3.1 Feature Extraction 12 3.3.2 Aggregation of Multi-level Features 12 3.3.3 Nearest Neighbor Search 13 Chapter 4 Experiments 17 4.1 Dataset 17 4.2 Implementation Details 18 4.3 Evaluation Metric 18 4.4 Results 19 4.4.1 Defect Detection 19 4.4.2 Defect Localization 20 4.5 Ablation Study 23 4.5.1 t-SNE Visualization 23 4.5.2 Feature Hierarchy 24 4.5.3 Local Smoothing 27 Chapter 5 Conclusion 28 REFERENCE 29 | - |
| dc.language.iso | en | - |
| dc.subject | 圖像異常檢測 | zh_TW |
| dc.subject | 特徵提取 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 自監督學習 | zh_TW |
| dc.subject | 圖像異常定位 | zh_TW |
| dc.subject | Feature extraction | en |
| dc.subject | Image anomaly localization | en |
| dc.subject | Image anomaly detection | en |
| dc.subject | Self-supervised learning | en |
| dc.subject | Deep learning | en |
| dc.title | 利用模擬異常進行自監督影像異常檢測及定位 | zh_TW |
| dc.title | Self-Supervised Image Anomaly Detection and Localization with Synthetic Anomalies | en |
| dc.title.alternative | Self-Supervised Image Anomaly Detection and Localization with Synthetic Anomalies | - |
| dc.type | Thesis | - |
| dc.date.schoolyear | 110-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 雷欽隆;王鈺強;余承叡 | zh_TW |
| dc.contributor.oralexamcommittee | Chin-Laung Lei;Yu-Chiang Wang; | en |
| dc.subject.keyword | 圖像異常檢測,圖像異常定位,自監督學習,深度學習,特徵提取, | zh_TW |
| dc.subject.keyword | Image anomaly detection,Image anomaly localization,Self-supervised learning,Deep learning,Feature extraction, | en |
| dc.relation.page | 33 | - |
| dc.identifier.doi | 10.6342/NTU202204080 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2022-09-28 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電機工程學系 | - |
| 顯示於系所單位： | 電機工程學系 | |
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