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研究生: 楊良偉
Yang, Liang-Wei
論文名稱: 應用於低成本單板電腦之有效人臉偵測設計
Efficient face detection design for low-cost single board computers
指導教授: 蘇崇彥
Su, Chung-Yen
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2019
畢業學年度: 107
語文別: 中文
論文頁數: 48
中文關鍵詞: 人臉偵測預選框設計單板電腦模型設計
英文關鍵詞: Face detection, Anchor box design, Single board computer, Model design
DOI URL: http://doi.org/10.6345/NTNU201900216
論文種類: 學術論文
相關次數: 點閱:131下載:2
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  • 人臉偵測(Face Detection)主要是在任意圖像中找到人臉的位置和大小的技術。早期人臉偵測演算法受到了燈光及遮蔽物影響其偵測準確率,隨著深度神經網路的發展已可以解決人臉偵測遇到的多數問題,並且獲得穩定的準確率,但是深度神經網路往往需要強大的硬體設備才能完成,昂貴的硬體設備並不符合許多實務的需求。單板電腦雖然價格低廉能夠大幅降低人臉偵測的門檻,但是單板電腦僅提供入門級的硬體設備,硬體的計算能力遠遠不及一般的電腦。
    本論文的研究目的是提出應用於低成本的單板電腦之有效人臉偵測網路設計,以降低人臉偵測硬體設備需求,減少計算量,並且維持人臉偵測的準確率。設計的內容主要包含使用模型權重量化,預選框設計、訓練集篩選,最後透過樹莓派3B來實現人臉偵測。實驗結果顯示本論文所提出的方法,除了能夠有效降低偵測時間外,並能增加2.7%的平均準確率,實際偵測一張影像的時間只需要0.3秒,達到近似及時人臉偵測的目的,並且成功地利用低成本的設備準確且快速的偵測人臉的所在位置。

    Face detection is a computer technology that identifies human faces in digital images. Light and facial occlusion affect the accuracy of face detection. Most face detection problems can be solved by deep learning model to get high accuracy. However, the use of deep learning model generally requires expensive and powerful devices. Single board computer is a cheaper device for face detection. Unfortunately, Single board computer is a less powerful device than general computers.
    In this study, we proposed a face detection model design for low-cost single board computers so that it can reduce computing power and increase detection accuracy. The detection model design uses the quantized model, a set of modified anchor boxes, the dataset analysis. The overall design is implemented on the Raspberry Pi 3B. The experimental results verify that our method can increases the average accuracy by 2.7 %, and its cost time requires only 0.3sec. The proposed method can effectively detect faces with the use of the low-cost device.

    摘要 I ABSTRACT II 目錄 III 圖目錄 V 表目錄 VIII 第一章 緒論 - 1 - 1.1 研究背景 - 1 - 1.2 研究動機 - 1 - 1.3 研究目的 - 2 - 1.4 論文架構 - 3 - 第二章 文獻探討 - 4 - 2.1 單板電腦相關文獻 - 4 - 2.2 人臉偵測相關文獻 - 5 - 2.2.1 哈爾級聯分類器 (Haar Cascade) - 7 - 2.2.2 多尺度串聯卷積網路 (MTCNN) - 8 - 2.2.3 單尺度偵測模型 (SSD) - 10 - 2.2.4 分離式卷積偵測模型 (MobileNet-SSD) - 12 - 2.3 模型權重量化介紹 - 14 - 第三章 實驗設計架構 - 17 - 3.1 硬體設備 - 17 - 3.2 開發環境 - 19 - 3.3 訓練人臉偵測模型 - 19 - 3.4 網路模型設計 - 21 - 3.4.1 人臉資料集整理 - 22 - 3.4.2 預選框與正樣本 - 23 - 3.4.3 預選框大小 - 25 - 3.4.4 預選框形狀 - 28 - 3.4.5 模型權重量化 - 31 - 第四章 實驗結果與分析 - 34 - 4.1 訓練模型與正樣本 - 35 - 4.2 預選框設計分析 - 36 - 4.3 模型權重量化分析 - 38 - 4.4 與現今人臉偵測演算法比較 - 40 - 4.5 偵測模型應用分析 - 42 - 第五章 結論與未來展望 - 43 - 參考文獻 - 44 - 自傳 - 47 - 學術成就 - 48 -

    [1] 人臉偵測參閱自維基百科全書。
    https://en.wikipedia.org/wiki/Face_detection
    [2] 深度學習解決方案透過GPU加速。NVIDIA。
    https://www.nvidia.com/zh-tw/deep-learning-ai/solutions/.
    [3] 2018年單板電腦Top 10。
    https://www.eettaiwan.com/news/article/20180914NT01-10-Best-Single-Board-Computers-2018
    [4] 針對不同應用需求 選擇不同構型單板電腦發展智能嵌入式應用。https://www.digitimes.com.tw/iot/article.asp?cat=130&id=0000397089_elo1dkma7s1enslvjjkp0
    [5] 樹莓派(Raspberry Pi),一款基於Linux的單晶片電腦。https://www.raspberrypi.org/
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    [15] chuanqi305 - Caffe implementation of Google MobileNet SSD detection network https://github.com/chuanqi305/MobileNet-SSD
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    [18] S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, D. Kalenichenko, “Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference,” arXiv:1712.05877v1, Dec. 2017
    [19] R. Krishnamoorthi, “Quantizing deep convolutional networks for efficient inference: A whitepaper,” arXiv:1806.08342v1, Jun. 2018
    [20] 微軟LifeCam HD-3000 網路攝影機系列。
    https://www.microsoft.com/accessories/zh-tw/products/webcams/lifecam-hd-3000/t3h-00014
    [21] M. Abadi, A. Agarwal et al., “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv:1603.04467, Mar. 2016.
    [22] TensorFlow Lite is a lightweight solution for mobile and embedded devices.
    https://www.tensorflow.org/lite
    [23] Tensorflow Object Detection API
    https://github.com/tensorflow/models/tree/master/research/object_detection
    [24] S. Yang, P. Luo, C. C. Loy, X. Tang, “WIDER FACE: A Face Detection Benchmark,” arXiv: 1511.06523v1, Nov. 2015
    [25] T. Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, P. Dollár, “Microsoft COCO: Common Objects in Context,” arXiv: 1405.0312, Feb. 2015
    [26] V. Jain, E. L. Miller, “FDDB: A Benchmark for Face Detection in Unconstrained Settings,” CVPR 2014

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