人工檢查的效率低無法趕上快速擴張的製造規模，更遑論人工檢查的高昂成本。當今計算機視覺蓬勃發展，在重複的大批量製造環境中，數據非常豐富，適合模型開發，機器學習模型透過模仿人類腦部神經元活動，對資料進行分析與推理，從而顯著降低成本，透過遠程檢查產品避免接觸物料，適合於智能製造領域中廣泛實施。因此，使用卷積神經網路（Convolutional Neural Network, CNN）是一個好的選擇。然而，最大的問題之一是標記數據的高成本，因此我們使用遷移學習，主要原理是應用各種任務共有的通用原理與抽象結構，以加速電腦的學習速度。本研究探討三種不同的方法:全層凍結的遷移學習(Transfer learning)、結合遷移學習與遷移學習結合隨機森林(Transfer learning + Random forest)與結合遷移學習與微調(Fine-tune)。通過各方法於圖像識別之表現，以比較三種不同模式。除了準確率外，本研究還使用精確率(Precision rate)、召回率(Recall rate)和F1分數作為整體衡量指標。經由數據分析的結果，我們觀察到在每種方法中，單獨使用遷移學習並不能獲得良好的性能。遷移學習是一種合適的中間工具，可幫助我們提取特徵並將非結構化細節轉換為向量，但它並不適合直接作為最終的分類器，它需要與其他方法結合以提高其準確率。

The low efficiency of manual inspection cannot keep up with the rapidly expanding manufacturing scale, not to mention the high cost of manual inspection.
Computer vision is booming nowadays. In the repeated mass manufacturing environment, the large amount of data is suitable for model development. Machine learning model works by imitating the interaction of brain neurons, thus significantly reducing costs. By remotely checking products to avoid contacting the materials, it is suitable for widespread implementation in the field of intelligent manufacturing. Using Convolutional Neural Network (CNN) is a suitable choice. However, one of the biggest problems is the high cost of labeling data. We adopt transfer learning to overcome the issue. The main concept of transfer learning is to apply common principles and structures to various tasks to accelerate the learning speed of computers. This study explores three different methods: full-layer frozen transfer learning, combination of transfer learning and feature extraction, and combination of transfer learning and fine-tuning. Evaluating each mode’s performance by image recognition. In addition to the accuracy rate, this study also uses the precision rate, recall rate and F1 score as the overall measurement indicators. According to the result of data analysis, we observe that in each method, using "transfer learning" alone does not achieve good performance.
Transfer learning is a suitable intermediate tool that can help us extract features and convert unstructured details into vectors, but it is not suitable for direct use as the final classifier. It needs to be combined with other methods to improve its accuracy.

圖目錄 3
表目錄 4
第一章 緒論 5
1.1 研究背景 5
1.2研究動機 5
1.3研究目的 6
1.4研究架構 6
第二章 相關文獻回顧 8
2.1卷積神經網路 8
2.2 遷移學習 10
2.3微調 11
2.4特徵萃取 12
2.5集成學習 12
2.5.1 決策樹 13
2.5.2裝袋演算法 14
2.5.3 隨機森林 14
2.6 Inception V3 15
2.7 ResNet簡介 18
2.7.1 ResNet背景 18
2.7.2 ResNet 19
第三章 研究方法 22
3.1 研究架構 22
3.2數據預處理 22
3.2.1 一位有效編碼 22
3.2.2 正規化 23
3.3 表現評估 23
3.4 遷移學習 25
3.4.1 遷移學習之應用 25
3.4.2 Inception V3之應用 26
3.4.3 ResNet50架構 28
3.5遷移學習結合隨機森林 30
3.6微調 31
第四章 數據分析 33
4.1個案問題 33
4.2 數據資料 33
4.3 使用遷移學習進行缺陷分類 34
4.3.1應用遷移學習 34
4.3.2 應用遷移學習結合隨機森林 37
4.3.3 應用微調 38
4.4 研究結果 40
第五章 結論 43
5.1 結論 43
5.2 未來研究 43
參考文獻 44

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