臺灣博碩士論文加值系統

本論文以軟性電子顯示器為檢測目標，希望通過數據科學來辨識瑕疵，提高AOI的效率，根據所提供的AOI圖像數據，來判斷瑕疵的分類，通過數據科學提高解讀效率，採用Keras為開發架構。實驗結果顯示，預訓練的MobileNet模型的測試準確率達到了 99%，透過剪枝與量化處理，模型大小可縮減至10分之1，可適合移植至嵌入式平台或者移動平台，有利於AOI設備之整合，使用CNN輔助AOI進行產品外觀控制，涵蓋人工檢測靈活性、機器控制高可靠性、錯誤率更低、識別速度更快。
深度學習技術利用神經網路，能將需辨識的圖像類型添加標籤，然後從這些圖像中識別出已知的功能特徵、異常和類別，以訓練系統模型，在訓練過程中，訓練系統識別明確定義的瑕疵、多種形式的缺陷，並研究物體的正常外觀，包括顯著但可接受的變化。作為科學和產業創新的領先技術之一，也越來越有益於人類的生活和生產，尤其是自動化控制和光學應用在人工智能等自動化趨勢的方向下炙手可熱，在各行領域下，人工智能目前為最有前途的行業之一，因此本文主要討論CNN影像分類，尤其以軟性電子顯示器為檢測目標，希望透過數據科學判讀瑕疵分類優化AOI。

This paper takes the flexible electronic display as the detection target, and hopes to identify defects through data science and improve the efficiency of AOI. According to the provided AOI image data, to judge the classification of defects, improve the interpretation efficiency through data science, and use Keras as the development framework . The experimental results show that the test accuracy of the pre-trained MobileNet model reaches 99%. Through pruning and quantization processing, the size of the model can be reduced to 1/10, which is suitable for porting to embedded platforms or mobile platforms, which is beneficial to AOI devices. The integration, using CNN-assisted AOI for product appearance control, covers the flexibility of manual inspection, high reliability of machine control, lower error rate, and faster recognition speed.
Deep learning technology uses neural networks to add labels to the image types to be identified, and then identify known functional characteristics, anomalies and categories from these images to train the system model. During the training process, the training system recognizes Well-defined imperfections, many forms of defects, and the study of the normal appearance of objects, including significant but acceptable changes. As one of the leading technologies of scientific and industrial innovation, it is also more and more beneficial to human life and production. In particular, automation control and optical applications are hot in the direction of automation trends such as artificial intelligence. In various fields, artificial intelligence is currently the It is one of the most promising industries, so this article mainly discusses CNN image classification, especially with flexible electronic displays as the detection target, hoping to interpret defect classification and optimize AOI through data science.

誌謝i
摘要ii
ABSTRACTiii
目次iv
表次vii
圖次ix
Chapter 1 緒論1
1.1研究背景1
1.2研究動機2
1.3研究範圍及限制3
1.4論文架構3
Chapter 2 相關研究4
2.1神經網路4
2.1.1深度網路4
2.1.2卷積神經網路5
2.1.3開發工具與模組5
2.2相關AOI 檢測之研究6
Chapter 3 研究方法9
3.1前置作業10
3.1.1開啟GPU10
3.1.2讀取資料10
3.1.3觀察圖片11
3.1.4處理資料不平衡12
3.1.5資料增強(Data Augmentation)12
3.1.6訓練圖片13
3.1.7Callbacks16
3.2模型剪枝 (Model pruning)16
3.3模型量化 (Quantization)17
3.3.1TFLiteConverter17
3.3.2Coremltools17
3.4其他模型 18
3.4.1MobileNetV118
3.4.2MobileNetV220
3.4.3InceptionV321
3.4.4Inception-ResNet-v222
Chapter 4 實驗結果23
4.1實驗結果23
4.1.1MobileNet24
4.1.2TFlite量化29
4.1.3mlmodel量化31
4.1.4多種神經網路32
4.2成果比較54
Chapter 5 結論與未來展望56
5.1結論56
5.2未來展望56
參考文獻 57

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