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題名 多重品質評估機制的樣品影像分類
Sample image classification with multiple quality evaluation mechanism
作者 林庭漪
Lin, Ting-Yi
貢獻者 羅崇銘
Lo,Chung-Ming
林庭漪
Lin,Ting-Yi
關鍵詞 瑕疵檢測
深度學習
機器學習
合成式學習
集成式學習
Defect detection
Deep learning
Machine learning
Synthetic learning
Ensemble learning
日期 2022
上傳時間 2-Sep-2022 15:00:17 (UTC+8)
摘要 德國提出工業4.0，其他國家進而也提出類似概念，融合實體與虛擬達到垂直與平行的整合，以智慧製造來實現工業4.0，其中，品質檢測在產品交付前占有重要的地位，為了快速且準確的全面檢測各種產品，基於影像辨識的高度自動化檢測是智慧製造中不可或缺的一環，本研究使用工廠實際量產中的6000張端子台零件樣本影像並建立一自動化人工智慧辨識模型。不同於過去文獻只使用單一分類器，本研究使用多重評估機制來建立辨識模型，透過紋理特徵擷取，形成機器學習模型，透過使用遷移式學習訓練不同卷積神經網路形成深度學習模型，並提出合成式學習，結合機器學習分類與深度學習分類的優點，同時評估使用機器學習、深度學習以及不同多重評估機制的比較。結果顯示以合成式學習中的Confidence進行多重評估機制的分類能將準確率提升最多，由96.00%提升至97.83%，集成式Bagging則是提供相對穩定的準確率提升，都比單一分類器提升0.7%左右，透過本研究得知，使用合成式學習與集成式學習建立的分類模型皆可達到提升準確率的目的，合成式學習在瑕疵檢測能夠達到最高準確率並實現智慧製造中的自動化。
Not only Germany proposed Industry 4.0, but also other countries have proposed similar concepts. Industry 4.0 is realized by integrating physical and virtual to achieve vertical and parallel integration and smart manufacturing. Quality inspection plays an important role before product delivery. In order to quickly and accurately inspect all kinds of products, highly automated inspection based on image recognition is an indispensable part of smart manufacturing. This study uses 6000 sample images of printed circuit board (PCB) connectors from actual mass production in factories and builds an automated artificial intelligence recognition model. Unlike previous literature that only uses a single classifier, this study uses multiple evaluation mechanisms to build the recognition model, which forms a machine learning model through texture feature extraction and a deep learning model through training different convolutional neural networks using transfer learning. This study proposes synthetic learning, which combines the advantages of machine learning and deep learning, and evaluates the comparison of using machine learning, deep learning, and different multiple evaluation mechanisms. The results show that using Confidence in synthetic learning to classify multiple evaluation mechanisms can improve the accuracy rate the most, from 96.00% to 97.83%, while Bagging provides a relatively stable accuracy rate improvement, both of which are about 0.7% higher than that of a single classifier. The synthetic learning can achieve the highest accuracy rate in defect detection and make the automation in smart manufacturing become practical.
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描述 碩士
國立政治大學
圖書資訊與檔案學研究所
109155019
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109155019
資料類型 thesis
dc.contributor.advisor 羅崇銘zh_TW
dc.contributor.advisor Lo,Chung-Mingen_US
dc.contributor.author (Authors) 林庭漪zh_TW
dc.contributor.author (Authors) Lin,Ting-Yien_US
dc.creator (作者) 林庭漪zh_TW
dc.creator (作者) Lin, Ting-Yien_US
dc.date (日期) 2022en_US
dc.date.accessioned 2-Sep-2022 15:00:17 (UTC+8)-
dc.date.available 2-Sep-2022 15:00:17 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2022 15:00:17 (UTC+8)-
dc.identifier (Other Identifiers) G0109155019en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141618-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 圖書資訊與檔案學研究所zh_TW
dc.description (描述) 109155019zh_TW
dc.description.abstract (摘要) 德國提出工業4.0，其他國家進而也提出類似概念，融合實體與虛擬達到垂直與平行的整合，以智慧製造來實現工業4.0，其中，品質檢測在產品交付前占有重要的地位，為了快速且準確的全面檢測各種產品，基於影像辨識的高度自動化檢測是智慧製造中不可或缺的一環，本研究使用工廠實際量產中的6000張端子台零件樣本影像並建立一自動化人工智慧辨識模型。不同於過去文獻只使用單一分類器，本研究使用多重評估機制來建立辨識模型，透過紋理特徵擷取，形成機器學習模型，透過使用遷移式學習訓練不同卷積神經網路形成深度學習模型，並提出合成式學習，結合機器學習分類與深度學習分類的優點，同時評估使用機器學習、深度學習以及不同多重評估機制的比較。結果顯示以合成式學習中的Confidence進行多重評估機制的分類能將準確率提升最多，由96.00%提升至97.83%，集成式Bagging則是提供相對穩定的準確率提升，都比單一分類器提升0.7%左右，透過本研究得知，使用合成式學習與集成式學習建立的分類模型皆可達到提升準確率的目的，合成式學習在瑕疵檢測能夠達到最高準確率並實現智慧製造中的自動化。zh_TW
dc.description.abstract (摘要) Not only Germany proposed Industry 4.0, but also other countries have proposed similar concepts. Industry 4.0 is realized by integrating physical and virtual to achieve vertical and parallel integration and smart manufacturing. Quality inspection plays an important role before product delivery. In order to quickly and accurately inspect all kinds of products, highly automated inspection based on image recognition is an indispensable part of smart manufacturing. This study uses 6000 sample images of printed circuit board (PCB) connectors from actual mass production in factories and builds an automated artificial intelligence recognition model. Unlike previous literature that only uses a single classifier, this study uses multiple evaluation mechanisms to build the recognition model, which forms a machine learning model through texture feature extraction and a deep learning model through training different convolutional neural networks using transfer learning. This study proposes synthetic learning, which combines the advantages of machine learning and deep learning, and evaluates the comparison of using machine learning, deep learning, and different multiple evaluation mechanisms. The results show that using Confidence in synthetic learning to classify multiple evaluation mechanisms can improve the accuracy rate the most, from 96.00% to 97.83%, while Bagging provides a relatively stable accuracy rate improvement, both of which are about 0.7% higher than that of a single classifier. The synthetic learning can achieve the highest accuracy rate in defect detection and make the automation in smart manufacturing become practical.en_US
dc.description.tableofcontents 摘要 i
Abstract ii
目次 iii
表次 v
圖次 vi
第一章 緒論 1
1.1 工業4.0時代下的智慧製造 1
1.2 品質檢測 2
第二章 文獻探討 5
2.1 機器學習應用於瑕疵檢測 5
2.2 深度學習應用於瑕疵檢測 7
2.3 多重評估機制的瑕疵檢測 8
第三章 材料與方法 11
3.1 端子台影像資料集 12
3.2 單一分類模型 14
3.2.1機器學習(ML) 15
3.2.2 深度學習(DL) 17
3.3 合成式學習(synthesis) 24
3.3.1 Probability 24
3.3.2 Feature 25
3.3.3 Confidence 26
3.4 集成式學習(DL ensemble) 28
3.4.1 Bagging 28
3.4.2 Stacking 29
第四章 結果 31
4.1 單一分類模型 31
4.1.1 機器學習(ML) 31
4.1.2 深度學習(DL) 32
4.2 合成式學習 33
4.2.1 Probability 33
4.2.2 Feature 34
4.2.3 Confidence 35
4.3 集成式學習 36
4.3.1 Bagging 36
4.3.2 Stacking 36
4.4影像分類結果 37
第五章 討論與結論 40
第六章 未來展望 42
參考文獻 43
附錄一 特徵類別 56
附錄二 特徵算法與介紹 59
附錄三 Classification Learner 中8大類分類演算法 72		zh_TW
dc.format.extent 3341748 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109155019en_US
dc.subject (關鍵詞) 瑕疵檢測zh_TW
dc.subject (關鍵詞) 深度學習zh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) 合成式學習zh_TW
dc.subject (關鍵詞) 集成式學習zh_TW
dc.subject (關鍵詞) Defect detectionen_US
dc.subject (關鍵詞) Deep learningen_US
dc.subject (關鍵詞) Machine learningen_US
dc.subject (關鍵詞) Synthetic learningen_US
dc.subject (關鍵詞) Ensemble learningen_US
dc.title (題名) 多重品質評估機制的樣品影像分類zh_TW
dc.title (題名) Sample image classification with multiple quality evaluation mechanismen_US
dc.type (資料類型) thesisen_US
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dc.identifier.doi (DOI) 10.6814/NCCU202201201en_US