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| 研究生： |
雷承維 Lei, Cheng-Wei |
|---|---|
| 論文名稱： |
非監督式深度學習系統應用於AOI檢測之研究 Unsupervised Deep Learning System for AOI Detection |
| 指導教授： |
黃文吉
Hwang, Wen-Jyi |
| 學位類別： |
碩士 Master |
| 系所名稱： |
資訊工程學系 Department of Computer Science and Information Engineering |
| 論文出版年： | 2020 |
| 畢業學年度： | 108 |
| 語文別： | 中文 |
| 論文頁數： | 45 |
| 中文關鍵詞： | 自動光學檢測 、異常檢測 、自編碼器 、深度學習 、類神經網路 、全卷積網路語義分割 |
| DOI URL： | http://doi.org/10.6345/NTNU202001296 |
| 論文種類： | 學術論文 |
| 相關次數： | 點閱：316 下載：70 |
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本論文提出並開發基於非監督式深度學習的表面瑕疵檢測系統，論文所提
出之研究內容，以檢測高階圖形處理器PCI Express金手指表面作為主要應用範例。
在開發平台上，本實驗以Python為主要系統建構語言；在深度學習實作
上，Python提供完整以及快速的開發工具，也提供相當充足的傳統影像處理演算法函式，讓實驗進行更為方便。
本實驗應用了Autoencoder模型的特性，即訓練實驗模型對目標影像的還原能力，檢測時經由比較輸入與輸出之間的差異來找出表面瑕疵。由於影像內容包含鍍金條、底板和部分PCB零件焊貼表面，在目標不僅侷限於鍍金條的表面部分的條件下，本實驗的最終重點在於如何使整體系統對不同表面內容擁有優良的檢測適性。
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