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研究生:洪子傑
研究生(外文):HUNG, TZU-CHIEH
論文名稱:基於物件偵測深度網路之低漏判率IC載板瑕疵檢測
論文名稱(外文):Defect Detection of IC Substrate with Low Loss Rate Based on Deep Object Detection Network
指導教授:陳金聖陳金聖引用關係
指導教授(外文):CHEN, CHIN-SHENG
口試委員:陳金聖林達德田方治何昭慶
口試委員(外文):CHEN, CHIN-SHENGLIN,DA-DETIEN, FANG-CHINHO, CHAO-CHING
口試日期:2020-07-21
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:自動化科技研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:118
中文關鍵詞:物件偵測深度網路IC載板瑕疵檢測低漏判率特徵融合低階視覺任務
外文關鍵詞:Deep Object Detection NetworkIC Substrate Defect DetectionLow Loss RateFeature FusionLow-level Vision Task
相關次數:
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摘 要 i
ABSTRACT ii
誌 謝 iii
目 錄 iv
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究方法 4
1.4 論文架構 9
第二章 文獻回顧 10
2.1 分類網路模型 11
2.1.1 ResNet 11
2.1.2 Inception-ResNet 14
2.2 物件偵測網路模型 18
2.2.1 R-CNN 19
2.2.2 Fast R-CNN 20
2.2.3 Faster R-CNN 21
2.2.4 Feature Pyramid Network 32
2.3 實例分割網路模型 34
2.3.1 Fully Convolutional Network 34
2.3.2 Mask R-CNN 35
2.3.3 Path Aggregate Network 37
2.4 基於深度學習在瑕疵檢測任務之應用 38
2.4.1 Classification Network 38
2.4.2 Tiny Defect Detection Network 40
第三章 研究方法原理與介紹 41
3.1 Small Anchor FPN 41
3.2 Reverse SA-FPN 45
3.3 Bi-direction SA-FPN 48
3.4 Path Aggregate SA-FPN 51
3.5 Reverse Path Aggregate SA-FPN 53
3.6 影像對比度調整(Image Contrast Adjustment) 55
3.7 在線難例挖掘(Online Hard Example Mining) 56
3.8 適應性特徵池化(Adaptive Feature Pooling) 57
3.9 全連接層不共享(FCL No Shared) 59
3.10 IC載板資料集 60
3.10.1 資料集來源 60
3.10.2 IC載板訓練集與測試集 61
3.10.3 多標籤分類問題 62
3.11 評估指標 65
3.11.1 混淆矩陣 65
3.11.2 F-Score 66
3.11.3 mAP 67
3.11.4 實驗統計標準 71
3.11.5 召回率與精準度關係 73
3.12 實驗環境 74
第四章 實驗結果與性能分析 75
4.1 實驗建置 75
4.2 實驗結果 82
4.2.1 Faster R-CNN實驗結果與分析 82
4.2.2 基底網路比較與分析 85
4.2.3 調整模組比較與分析 88
4.2.4 網路模型比較與分析 99
4.2.5 實驗總結 109
第五章 結論與未來展望 111
5.1 結論 111
5.2 未來展望 113
參考文獻 114
附錄1-模型訓練與測試時間統計表 118
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