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研究生： 鄭登元
Deng-Yuan Zheng
論文名稱： 基於神經網路之即時結帳系統
Real Time Checkout System Based on Neural Network
指導教授： 林其禹
Chi-Yu Lin
口試委員: 徐繼聖
Ji-Sheng Shiu
林柏廷
Bo-Ting Lin
學位類別： 碩士
Master
系所名稱： 工程學院 - 機械工程系
Department of Mechanical Engineering
論文出版年： 2021
畢業學年度： 109
語文別： 中文
論文頁數： 76
中文關鍵詞： 自助結帳 、深度學習 、電腦視覺 、人工智慧 、機器學習
外文關鍵詞： Self-checkout, Deep learning, Neural Network, Computer Vision, Convolutional Neural Network
相關次數： 點閱：315  下載：3 
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    • 傳統智慧商店的自助結帳系統是基於RFID或條碼技術，但在結帳過程中需要逐一找尋商品中條碼再進行掃描，過程過於耗時與繁瑣。RFID訊號容易因遮蔽而導致偵測失敗，並需要在每項商品中貼上標籤，增加每項商品成本。
    本論文研究擬開發一套可供顧客自行擺放商品後可快速自動結帳的智慧視覺系統。本智慧視覺系統使用物件辨識神經網路(Object Detection Neural Network)中的YOLO(You only look once)網路架構進行商品辨識，同時使用MTCNN(Multi-task Cascaded Convolutional Networks)網路架構進行人臉辨識，藉由有效的人機介面GUI(Graphical User Interface)設計，使消費者可自行結帳，建立一套省時、安全、便利的智慧結帳系統。
    由於深度學習需要標註大量的商品，並耗費時間與成本，本論文同時開發出全自動標註器(Auto-labeler)技術，可大幅縮減深度學習前置作業需要標註耗費人力、時間的問題。

    Traditional self-checkout systems in smart stores are based on RFID or barcode technology, but in the checkout process, it will demand a time-consuming process of finding the barcode of each product and scan it. RFID signals can easily fail to be detected due to poor tag placement, and it is necessary to attach a tag to each product, which increases the cost of each product.
    In this thesis, an intelligent vision-based customer operated check-out system is proposed. This system comprises a YOLO (You only look once) network architecture, which is one of Object Detection Neural Networks (ODN), for product identification and an MTCNN (Multi-task Cascaded Convolutional Networks) network architecture for customer face identification. A Graphical User Interface (GUI) is designed to bundle al systems to enable the function that consumers can conduct the checkout process by themselves to achieve a time-saving, secure and convenient checkout operation.
    Since building an effective deep learning system which requires the labeling process on each of a large number of images of products is time-consuming and costly, this thesis also develop an automatic labeling system to significantly reduce the time and labor required for the deep learning pre-processing.

    摘要 Abstract 目錄 圖目錄 表目錄 第一章 緒論 1-1 前言 1-2 研究動機與目的 1-3 文獻回顧 1-4 本文架構 第二章 研究基礎理論 2-1 多層感知器(Multilayer Perceptron, MLP) 2-1-1 反向傳播演算法(Back Propagation) 2-1-2 激活函數(Activation Functions) 2-1-3 損失函數(Loss Function) 2-1-4 梯度下降法(Gradient Descent) 2-2 卷積神經網路(Convolution Neural Network, CNN) 2-2-1 卷積層(Convolutional Layer) 2-2-2 池化層(Pooling Layer) 2-2-3 全連接層(Fully-Connected Layer) 2-3 YOLOv4 即時偵測系統 2-3-1 Backbone-CSPDarknet53 2-3-2 Cross Stage Partial Network(CSPNet) 2-3-3 Neck (SPP+PANet) & YOLO HEAD 2-3-4 YOLOv3的損失函數 2-3-5 YOLO4的損失函數 2-4 BASNet(Boundary Aware Salient Object Detection) 2-4-1 BASNet網路架構 2-4-2 BASNet的損失函數 2-5 批次標準化(Batch Normalization) 2-6 影像處理方法 2-6-1 影像二值化 2-6-2 形態學(Morphology) 2-6-3 圓偵測 2-6-4 邊緣偵測 第三章 研究方法設計 3-1 即時結帳系統環境設計 3-1-1 即時結帳系統人機介面設計 3-1-2 即時結帳系統硬體設計 3-2 即時結帳辨識架構 3-2-1 即時結帳系統架構 3-2-2 商品數據標註與訓練 3-2-3 商品數據增強(Data Augmentation) 3-3 自動標註器商品數據架構 3-3-1 自動標註器 第四章 實驗結果與分析 4-1 實驗設備 4-1-1 攝像鏡頭 4-1-2 實驗開發環境 4-1-3 硬體設備架構 4-2 即時商品辨識實驗說明 第五章 結論與未來展望 5-1 結論 5-2 未來展望 參考文獻

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