研究生: |
王雅慶 Wang, Ya-Ching |
---|---|
論文名稱: |
以FPGA實現摺積神經網路及應用於人臉辨識之研究 The implementation of CNN-based face recognition systems based on FPGA |
指導教授: |
吳榮根
Wu, Jung-Gen 黃文吉 Hwang, Wen-Jyi |
學位類別: |
碩士 Master |
系所名稱: |
資訊工程學系 Department of Computer Science and Information Engineering |
論文出版年: | 2016 |
畢業學年度: | 104 |
語文別: | 中文 |
論文頁數: | 78 |
中文關鍵詞: | 摺積神經網路 |
DOI URL: | https://doi.org/10.6345/NTNU202204484 |
論文種類: | 學術論文 |
相關次數: | 點閱:201 下載:42 |
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本研究主要提出一個以可程式化邏輯閘陣列(Field Programmable Gate Array; FPGA) [1]為主的硬體架構來實現快速辨識影像架構,此架構是採用摺積神經網路(Convolutional Neural Network ; CNN)的向前傳遞法則(Forward propagation)來實現影像的辨識階段,現有的CNN系統架構多以GPU實現,GPU有高功率的缺點,而現有使用FPGA實現CNN運算的電路設計大部分只有設計CNN中的少數幾層,只實作出摺積層或是全連結層,本研究以FPGA為平台,設計CNN中的Lenet5模型,設計出Lenet5完整架構,具有低功率消耗跟極高的辨識率的優點。
本研究的架構為可程式化系統晶片(System on Programmable Chip; SOPC)中的硬體加速器以實現圖像辨識,本研究使用人臉圖像來當作辨識影像,總共辨識28個人的人臉。實驗結果顯示本研究所提出的CNN架構十分合適於使用在需要高可攜性,高辨識率,高計算速度等的視覺應用。本論文實作CNN的Lenet5架構比較適合運用在社區的人臉監視系統,Lenet5 模型對於很多人的辨識運用比其它摺積神經網路較差些,像是VGG Net [2]、GOOGLE Net [3],但對於30人左右的辨識率Lenet5模型還是辨識率還是足夠的。本研究可以使用在社區人臉辨識,社區的人臉監視系統只需要辨識社區內所有人物,而且辨識的速度快速,一有辨識錯的影像可以馬上被察覺,不會讓社區以外的人進入,這是本論文的一個有趣的應用。
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