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研究生:李佳芳
研究生(外文):LI, JIA-FANG
論文名稱:基於YOLO模型的時尚圖像物件辨識研究
論文名稱(外文):Fashion Image Object Detection Research Based on YOLO Model
指導教授:邱垂昱邱垂昱引用關係
指導教授(外文):CHIU, CHUI-YU
口試委員:鄭辰仰楊神珠邱垂昱
口試委員(外文):CHENG, CHEN-YANGYANG, SHEN-CHUCHIU, CHUI-YU
口試日期:2020-07-08
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:工業工程與管理系
學門:工程學門
學類:工業工程學類
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:68
中文關鍵詞:深度學習YOLO物件辨識
外文關鍵詞:Deep LearningYOLOObject Detection
相關次數:
  • 被引用被引用:1
  • 點閱點閱:496
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
摘 要 i
ABSTRACT ii
誌謝 iv
目 錄 v
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 2
1.3 研究範圍與限制 2
1.4 研究流程 3
第二章 文獻探討 5
2.1 深度學習 5
2.1.1 深度學習之背景 5
2.1.2 深度學習之應用 8
2.2 卷積神經網路 9
2.2.1 卷積神經網路介紹 9
2.2.2 卷積神經網路架構 11
2.3 物件偵測 13
2.3.1 物件偵測介紹 13
2.3.2 定位方式 13
2.3.3 YOLOv1 14
2.3.4 YOLOv2 19
2.4 數據擴充 23
2.5 相關研究 24
2.6 小結 26
第三章 研究方法 28
3.1 研究架構 28
3.2 資料蒐集 29
3.3 資料物件項目 29
3.4 資料前處理 30
3.5 YOLOv3 32
3.6 績效指標 35
第四章 研究結果與分析 37
4.1 實驗環境設定 37
4.2 實驗樣本與參數設定 37
4.3 敏感度分析 38
4.4 實驗結果 38
4.4.1 物件偵測結果 38
4.4.2 物件辨識結果 44
4.4.3 學習率的敏感度分析 52
第五章 結論與建議 56
5.1 研究結論 56
5.2 未來方向與建議 58
參考文獻 59
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