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研究生:葉昕語
研究生(外文):YEH, SHIN-YEU
論文名稱:智慧購物車之基於深度學習商品辨識系統設計
論文名稱(外文):The Design of Deep-Learning-Based Commodity Identification System for Smart Shopping Cart
指導教授:鄭穎仁
指導教授(外文):CHENG, YING-JEN
口試委員:羅吉昌蔡舜宏楊棧雲鄭穎仁
口試委員(外文):LO, JI-CHANGTSAI, SHUN-HUNGYANG, CHAN-YUNCHENG, YING-JEN
口試日期:2021-08-11
學位類別:碩士
校院名稱:國立臺北大學
系所名稱:電機工程學系
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:2021
畢業學年度:109
語文別:英文
論文頁數:43
中文關鍵詞:商品辨識影像處理神經網路資料庫高斯混合模型
外文關鍵詞:Commodity identificationImage processingNeural networkDatabaseGaussian mixture model
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  • 被引用被引用:0
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  • 下載下載:37
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本研究主旨在於設計一個基於機器視覺和深度學習技術的商品辨識系統,並且希望用於智慧購物車上。現在科技發展的非常快速,人工智能技術廣泛應用於我們的生活中,它讓我們節省了許多時間與人力,也使許多事情變得更加便捷。由於技術的進步,人工智能與物聯網相繼結合,成為智能物聯網,並且與機器人結合應用,我們也逐漸可以在市面上看到它的身影。因此我們打算設計一個商品辨識系統,並結合物聯網技術,應用於智慧購物車。本文首先著重於使用機器視覺找出置物平台上的商品,使用的是高斯混合模型搭配其他影像處理技術,分割新放入購物車中之獨立商品影像。之後,利用自行收集的商品影像,應用於Inception深度神經網路,對商品分類進行訓練。最後將兩者結合於即時影像中,達到商品即時辨識的目的,並結合資料庫,將購物車中之商品項目及價格顯示於使用者介面之中。
The main purpose of this research is to design a commodity identification system based on machine vision and deep learning technology for applying to smart shopping carts. Nowadays, science and technology are developing very fast. Due to the advancement of technology, artificial intelligence (AI) and the Internet of Things (IoT) have successively combined to become the AIoT. Combining AIoT with robots, we can gradually see applications on the market. Therefore, we intend to design a commodity identification system combining with the IoT technology for applying to smart shopping carts. This thesis first focuses on the use of machine vision to extract the commodities on the storage platform of shopping cart. The Gaussian mixture model with other image processing technologies are applied to segment the image of the commodity newly placed in the storage platform. After that, the self-collected images of commodities are applied to train the GoogLeNet deep neural network for commodity identification. After that, the two approaches are combined for the purpose of real-time commodity identification. Finally, with the aid of database, the list of commodities with prices is shown in the user interface.
Acknowledgment i
ABSTRACT iii
Table of Contents iv
List of Figures vi
List of Tables vii
CHAPTER 1 1
INTRODUCTION 1
1.1 BACKGROUND 1
1.2 MOTIVATION AND MAIN TASKS 5
1.3 ORGANIZATION 5
CHAPTER 2 7
PRELINNINARY 7
2.1 GAUSSIAN MIXTURE MODEL 7
2.2 INCEPTION 9
2.2.1 Inception V1 10
2.2.2 Inception v2 and Inception v3 12
2.2.3 Inception-ResNet-V2 13
2.3 DATABASE 14
CHAPTER 3 16
SYSTEM STRUCTURE 16
3.1 PHYSICAL DEVICEs 17
3.2 SYSTEM ENVIRONMENT AND SOFTWARE 18
CHAPTER 4 19
COMMODITY IDENTIFICATION SYSTEM DESIGN 19
4.1 The Gaussian Mixture Model for Extracting Commoditiy Images 20
4.3 deep-learning-Based Commoditiy identification 25
4.4 Database link and user interface 27
CHAPTER 5 29
EXPERIMENTAL RESULTS 29
5.1 Commodity Image extraction results 29
5.2 Commodity identification result 31
5.3 Demonstration of commodity indetification 34
CHAPTER 6 38
CONCLUSION 38
REFERENCES 39

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