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| Title: | 多重品質評估機制的樣品影像分類
Sample image classification with multiple quality evaluation mechanism |
| Authors: | 林庭漪
Lin, Ting-Yi |
| Contributors: | 羅崇銘
Lo,Chung-Ming
林庭漪
Lin,Ting-Yi |
| Keywords: | 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 |
| Date: | 2022 |
| Issue Date: | 2022-09-02 15:00:17 (UTC+8) |
| Abstract: | 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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| Description: | 碩士
國立政治大學
圖書資訊與檔案學研究所
109155019 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109155019 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202201201 |
| Appears in Collections: | [圖書資訊與檔案學研究所] 學位論文
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