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中文論文名稱 基於深度學習之表面缺陷識別系統設計
英文論文名稱 Design of a Surface Defect Recognition System Based on Deep Learning
校院名稱 淡江大學
系所名稱(中) 電機工程學系機器人工程碩士班
系所名稱(英) Master’s Program In Robotics Engineering, Department Of Electrical And Computer Engineering
學年度 108
學期 2
出版年 109
研究生中文姓名 陳浩瑋
研究生英文姓名 Hao-Wei Chen
學號 607470043
學位類別 碩士
語文別 中文
口試日期 2020-07-17
論文頁數 58頁
口試委員 指導教授-蔡奇謚
共同指導教授-許駿飛
委員-許陳鑑
委員-李世安
委員-蔡奇謚
中文關鍵字 深度學習  監督式端到端學習  表面缺陷識別  SurfNet  矽酸鈣板 
英文關鍵字 Deep Learning  Supervised End-To-End learning  Surface Defect Recognition  SurfNet  Calcium Silicate Boards 
學科別分類
中文摘要 缺陷識別在現代工業中已是不可或缺的一部分,如何以機器取代人力是此領域的重要課題。本文提出一種基於深度學習方法的表面缺陷識別系統,該系統透過設計的高速圖像擷取平台捕捉RGB圖像信息,並透過改進的卷積神經網絡(CNN)架構來識別表面缺陷。基於SurfNet架構得到的靈感,系統中提出的CNN模型命名為SurfNetv2,它包括一個特徵提取模塊和一個缺陷識別模塊。我們將提出的網路模型應用於矽酸鈣板(CSB)表面缺陷識別任務上,並通過端到端有監督式學習方法來訓練模型。我們定義矽酸鈣板表面上的缺陷類別分為四類,即崩潰、髒污、不均勻和正常。當模型處理解析度為128×128的圖像時,實現了約199.38 fps的實時計算速度,並分別在私有矽酸鈣板表面缺陷數據集、公共NEU數據集和公共DeepPCB實驗中,實現了約99.90%、99.75%和91.15%的準確率。實驗結果證明提出的系統可以做到矽酸鈣板表面缺陷實時識別。
英文摘要 Defect detection has become an indispensable part of modern industry. How to replace manpower with machines is an important issue in this field. This thesis proposes a surface defect recognition system based on deep learning method. The system captures RGB image information through a designed high-speed image acquisition platform, and recognizes surface defects through an improved convolutional neural network (CNN) architecture. Inspired by the SurfNet architecture, the CNN model proposed in the thesis is named SurfNetv2, which includes a feature extraction module and a defect recognition module. We applied the proposed network model to the surface defect recognition task of calcium silicate board (CSB), and trained the model through an end-to-end supervised learning method. The recognized surface defect types of CSB are divided into four categories, namely crash, dirty, uneven and normal. The model achieves a real-time calculation speed of about 199.38 fps when processing images with a resolution of 128×128. It also achieves the accuracy rates about 99.90%, 99.75% and 91.15% in the experiments of the private CSB surface defect dataset, the public NEU dataset and the public DeepPCB dataset, respectively. Experimental results show that the system can realize real-time identification of CSB surface defects.
論文目次 目錄
中文摘要........................I
英文摘要........................II
目錄............................III
圖目錄..........................VI
表目錄..........................VIII
第一章 序論.....................1
1.1 研究背景...................1
1.2 研究動機與目的.............3
1.3 系統架構...................4
1.3.1 硬體....................5
1.3.2 軟體流程................6
1.4 論文架構...................7
1.5 論文貢獻...................8
第二章 相關研究.................10
2.1 缺陷識別...................10
2.1.1 機器視覺方法............10
2.1.2 頻譜分析................12
2.1.3 色彩空間轉換............13
2.2 深度學習方法...............14
2.2.1 語意分割................14
2.2.2 偵測....................15
2.2.3 分類....................16
2.2.4 現有方法改進............16
2.3 卷積神經網路...............17
2.4 文獻總結...................18
第三章 提出的表面缺陷識別模型...20
3.1 特徵擷取層.................20
3.1.1 卷積模塊................20
3.1.2 殘差模塊................22
3.1.3 特徵擷取模塊............23
3.2 缺陷識別層.................24
3.2.1 全局平均池化層..........25
3.2.2 輸出層..................25
3.3 表面缺陷識別網路模型.......26
3.3.1 SurfNetv2...............27
3.3.2 額外的架構設計..........28
3.4 訓練.......................30
第四章 實驗結果與分析...........32
4.1 軟硬體介紹.................32
4.2 數據集.....................33
4.2.1 數據收集................33
4.2.2 數據集創建..............34
4.2.3 訓練數據集..............35
4.3 實驗模型與性能驗證.........38
4.4 實驗分析...................41
4.4.1 私有矽酸鈣板缺陷數據集..41
4.4.2 公共NEU缺陷數據.........44
4.4.3 DeepPCB.................47
4.4.4 小結....................48
4.5 SurfNetv2模塊數量驗證......49
第五章 結論與未來展望...........50
參考文獻........................52

圖目錄
圖1.1 機器視覺的構成要素................................2
圖1.2 矽酸鈣板表面缺陷識別透過人工檢查方式..............3
圖1.3 高速圖像擷取平台..................................5
圖1.4 提出的表面缺陷識別系統架構........................6
圖3.1 SurfNet基礎卷積模塊...............................21
圖3.2 SurfNetv2基礎卷積模塊.............................21
圖3.3 SurfNet殘差模塊...................................22
圖3.4 SurfNetv2殘差模塊.................................22
圖3.5 SurfNet特徵擷取模塊...............................23
圖3.6 SurfNetv2特徵擷取模塊.............................23
圖3.7 多類別表面缺陷識別模型............................24
圖3.8 SurfNet網路模型架構...............................27
圖3.9 SurfNetv2網路模型架構.............................28
圖4.1 矽酸鈣板表面缺陷樣本收集..........................33
圖4.2 私有矽酸鈣板表面缺陷數據集的四個類別;
從左上到右下分別為: 正常、不均勻、髒污和碰撞......34
圖4.3 應用多個仿射變換來增強數據........................35
圖4.4 公共NEU數據集類別.................................37
圖4.5 公共DeepPCB數據集類別.............................38

表目錄
表 3.1 SurfNetv2與額外的模型架構........29
表 4.1 軟硬體規格.......................32
表 4.2 私有矽酸鈣板表面缺陷數據集.......36
表 4.3 公共NEU數據集....................36
表 4.4 公共DeepPCB數據集................37
表 4.5 實驗模型架構.....................39
表 4.6 實驗模型訓練epoch設定............40
表 4.7 私有矽酸鈣板缺陷數據集的實驗結果.43
表 4.8 公共NEU數據集的實驗結果..........46
表 4.9 公共DeepPCB數據集的實驗結果......48
表 4.10 不同SurfNetv2模塊下的性能驗證...49



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