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研究生:林哲維
研究生(外文):Lin, Che-Wei
論文名稱:多重損失評估卷積網路於影像內容分割之應用
論文名稱(外文):Multi-loss Convolutional Networks for Semantic Segmentation
指導教授:陳煥宗
指導教授(外文):Hwann-Tzong Chen
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:25
中文關鍵詞:卷積類神經網路影像內容分割多重損失評估
外文關鍵詞:Convolutional Neural NetworkSemantic SegmentationMulti-loss
相關次數:
  • 被引用被引用:0
  • 點閱點閱:257
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  • 下載下載:45
  • 收藏至我的研究室書目清單書目收藏:0
本論文研究的主題是場景物件辨識與切割。透過訓練深層卷積類神經網路,讓 照片中的每個像素都能夠被分類。與傳統非類神經網路為主的學習方法不同的是, 不需要取大量不同的特徵向量對於訓練類神經網路,我們提高了丟失率 (dropout rate) 以及利用不同的能量函數來衡量類神經網路的效能,比單一能量函數的衡量更 能提高對於小物件的辨識率。接著利用前述方法對於不同類別都有很高偵測率的特 性,設計了一套利用擴張小物體面積來讓小物體能更顯著被分類的方法。
最後藉由實驗結果探討改進之處,發現擴張小物體面積的技巧對於物體分割 的準確度的關係,以及了解多重能量函數對於類神經網路能否同時執行多項不同的 作業有無幫助。
This thesis presents a semantic segmentation method based on fully-convolutional network (FCN). We focus on increasing mean-class accuracy by adding other steps that help FCN to find more small objects: i) modulating the dropout rates, ii) combining multiple loss functions, and iii) expanding small object areas.
Our approach shows that the above steps can significantly increase mean-class accuracy without sacrifice too much per-pixel accuracy. We also provide experimental observations on the relationship between the area-expanding method and the CNN model. Finally, we discuss how to improve the workflow and what we have learned from the experiments of training with multi-loss functions.
1 Introduction 7
1.1 RelatedWork ................................. 8
2 Semantic Segmentation 10
2.1 Neuralnetworks................................ 10
2.1.1 Architecture.............................. 10
2.1.2 Losses................................. 11
2.1.3 Dropoutrate ............................. 11
2.2 Small area expansion ............................. 12
3 Experiments 13
3.1 Porting FCN Model.............................. 13
3.1.1 Tuning dropout rate.......................... 14
3.1.2 Multi-loss neural network training.................. 15
3.1.3 Deciding the area expansion thresholds . . . . . . . . . . . . . . . 16
3.1.4 Performance ............................. 16
4 Conclusion
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