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研究生:宋家緯
研究生(外文):SUNG, CHIA-WEI
論文名稱:深度學習應用於影像辨識之來令片輪廓與位置分析
論文名稱(外文):Using Deep Learning for Image Recognition of Brake Pad Contours and Positions
指導教授:陳志維陳志維引用關係
指導教授(外文):CHEN, JYH-WEI
口試委員:陳志維蘇琨祥蔡貴義
口試委員(外文):CHEN, JYH-WEISU, KUN-SIANGCAI, GUEI-YI
口試日期:2020-07-30
學位類別:碩士
校院名稱:國立虎尾科技大學
系所名稱:車輛工程系碩士班
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:54
中文關鍵詞:深度學習來令片影像辨識影像校正模型訓練
外文關鍵詞:TensorflowOpenCVImage recognitionBrake padDeep learning
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本研究藉由深度學習技術及影像辨識結合,設計一套對剎車來令片檢測判斷輪廓與位置系統。剎車來令片製造過程中因製程關係導致會有膨脹、缺角…等問題必須先找出其輪廓與位置,本研究過程先將剎車來令片分為輪廓並拍照建立圖檔,接著進行影像處理去除主體以外部分,最後將處理過的圖檔做標記並輸入至神經網路中訓練,透過損失函數觀察模型訓練狀況。本論文提出的方法可以有效達到透過影像處理解決背景雜亂多干擾問題、判別剎車來令片輪廓、自動矯正影像之目的。
In this thesis, deep learning image recognition with Tensorflow and image processing (OpenCV) are used to calculate contours and positions of brake pads for automatic detection system. In the process of brake pad manufacture, it may result in some problems, such as expansion, missing corners. Primary step is to determine contours and positions of brake pads by taking their picture to establish databases. Then, image recognition is used by using deep learning to train the contours and positions of the brake pads. Finally, the pre-processed images are labeled and input into the neural network for training. The images are evaluated through loss function in order to observe the training process of the models. The proposed method in this thesis can effectively achieve the following objectives: Solve the problem of the background multiple obstructions through image recognition; determine the brake pad contours and positions; automatically correct the image.
摘要......................................................i
Abstract..................................................ii
誌謝......................................................iii
目錄......................................................iv
圖目錄.................................................... vi
第一章 緒論..............................................1
1.1 研究動機與目的..........................................1
1.2 文獻探討...............................................3
1.3 論文大綱...............................................4
第二章 深度學習與影像辨識技術介紹...........................5
2.1 類神經網路(Neural Network , NN).........................5
2.2 卷積神經網路(Convolutional Neural Network, CNN).........9
2.3 反傳遞演算法(Back propagation)..........................12
2.4 影像處理(Image Recognition).............................14
第三章 研究架構與實驗方法...................................19
3.1 研究架構................................................19
3.2 資料蒐集................................................20
3.3 影像預處理..............................................21
3.4 尋找邊緣................................................25
3.5 資料預處理...............................................28
3.6 建立模型................................................30
第四章 實驗與結果探討.......................................35
4.1 尋找輪廓與模糊化之關係實驗結果.............................35
4.2 尋找來令片邊緣區域實驗結果.................................37
4.3 手動標記尋找邊緣實驗結果...................................38
4.4 模型實驗結果..............................................41
第五章 結論與未來研究方向.....................................46
5.1 結論......................................................46
5.2 未來研究方向...............................................47
參考文獻.......................................................48
Extended Abstract............................................50



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