智能零售店已經(jīng)成為吸引許多科技公司的關(guān)注的活躍話題。亞馬遜、深蘭科技奧蘭治和IBM等公司已經(jīng)開始通過搭建完整的智能零售店或零售柜來提高客戶的購物體驗(yàn)。這些公司正在使用較為復(fù)雜的集成系統(tǒng)來實(shí)現(xiàn)其目標(biāo)。但是,在考慮具體搭建一個小規(guī)模的智能零售店場景或零售柜時,首先需要解決一些關(guān)鍵問題:如何生成和標(biāo)記屬于某個域的所需圖像數(shù)據(jù)集,比如,擬售商品在不同視角下的拍照圖像;設(shè)計用于所售商品檢測識別機(jī)器學(xué)習(xí)輕量化模型,它要同時滿足精度、速度和存儲容量等多個方面的要求;設(shè)計基于客戶人臉圖像的年齡估計和性別識別的輕量化機(jī)器學(xué)習(xí)模型。本文在較為全面地整理歸納智能零售店和零售柜的基礎(chǔ)上,提出了一款智能零售柜總體設(shè)計方案,并就其中的三個關(guān)鍵問題,進(jìn)行較為深入的研究,提出并實(shí)現(xiàn)了相應(yīng)的解決方案。為了生成用于深度學(xué)習(xí)訓(xùn)練的帶標(biāo)注的圖像數(shù)據(jù)集,本文設(shè)計了一條簡單高效的人機(jī)協(xié)同的處理流水線。首先,對于每一類擬出售的商品,通過人工拍照或其它渠道,采集得到包含有該商品的圖像,并賦予商品的類標(biāo)屬性,形成初步的圖像數(shù)據(jù)集。隨后,從每類商品中隨機(jī)選擇小部分圖像,采用預(yù)訓(xùn)練的Mask RCNN模型,生成可疑目標(biāo)的邊界外框,經(jīng)過人工... 

【文章來源】：廈門大學(xué)福建省 211工程院校 985工程院校 教育部直屬院校

【文章頁數(shù)】：96 頁

【學(xué)位級別】：碩士

【文章目錄】：
摘要
Abstract
Chapter 1 Introduction
    1.1 Future of Smart Retail Market
    1.2 Challenges
        1.2.1 Image Data Annotation
        1.2.2 Design Lightweight Model
        1.2.3 Base Features for Product Recommendation
    1.3 Related Work
        1.3.1 Recent Work in Smart Retail Stores
        1.3.2 Literature for Product Detection and Product Classification
        1.3.3 Related Work in Age and Gender Estimation for ProductRecommendation
    1.4 Our Solutions for the Challenges
        1.4.1 Training Image Dataset
        1.4.2 Naive Bounding Box Annotation Pipeline
        1.4.3 Product Detection
        1.4.4 Product Recommendation
    1.5 Contributions
    1.6 Structure of the Thesis
Chapter 2 System Architecture of the Smart Retail Cabinet
    2.1 Environment Designs for Smart Retail Cabinet
    2.2 System Diagram
    2.3 Logical Diagram
        2.3.1 Product Detection
        2.3.2 Product Recommendation
    2.4 Summary
Chapter 3 Naive Bounding Box Annotation
    3.1 Preparing Custom Image Dataset
    3.2 Bounding Box Annotation is a Challenge
    3.3 Overcome the Challenge
    3.4 Motivation
    3.5 Stage 1-Classification Models
        3.5.1 Preparing Cropped Images
        3.5.2 Features for Training Classification Model
        3.5.3 Image Augmentation
        3.5.4 Pre-Trained Feature Extraction Model
        3.5.5 Training the Classification Models
        3.5.6 Results for Classification Models
    3.6 Stage 2-Naive bounding Box Annotation Pipeline
        3.6.1 Pre-trained Object Detector
        3.6.2 Bounding Box Annotation Process
        3.6.3 Experiment for Naive Bounding Box Annotation Pipeline
        3.6.4 Results for Naive Bounding Box Annotation Pipeline
    3.7 Summary
Chapter 4 Product Detection in Smart Retail Cabinet
    4.1 One-Staged object detectors
    4.2 Product Detection Flow in Smart Retail Cabinet
    4.3 Evaluation Metric
    4.4 Object Detection Models for Smart Retail Cabinet
        4.4.1 SSD Models: MobileNetV2
        4.4.2 SSD Models: VGG16
        4.4.3 Tiny-YOLO V2
        4.4.4 Training and Experiment
        4.4.5 Results for Object Detection Models
    4.5 Lightweight Models for Smart Retail Cabinet
        4.5.1 MobileNet Architecture for YOLO algorithm
        4.5.2 Designing a Custom Module
        4.5.3 Custom Module inside YOLO model
        4.5.4 Custom Model Architecture
        4.5.5 Training Details for Custom Model
        4.5.6 Experimental Results
    4.6 Product Tracking in Smart Retail Cabinet
        4.6.1 Split Screen
        4.6.2 Movement Direction
        4.6.3 Counters
        4.6.4 Centroid Tracking Algorithm
        4.6.5 Drawbacks of the Tracking Algorithm
        4.6.6 Results with Product Tracking
    4.7 Summary
Chapter 5 Product Recommendation for Smart Retail Cabinet
    5.1 Adience Face Image Dataset
        5.1.1 Attributes of Adience Face Image Dataset
        5.1.2 Pre-processing
    5.2 Face Detection for Product Recommendation
    5.3 Face Detection Model in Smart Retail Cabinet
    5.4 Age and Gender Estimation in Smart Retail Cabinet
        5.4.1 Age and Gender estimation model
        5.4.2 Objective Function for Age and Gender Estimation
        5.4.3 Training the Models
        5.4.4 Results for Age and Gender Estimation
        5.4.5 Age Estimation-Precision Recall Curves
        5.4.6 Gender Estimation-ROC Curves
        5.4.7 Results on Test Images
        5.4.8 Results on Other Images
    5.5 Summary
Chapter 6 Conclusion and Future Work
    6.1 Summary
    6.2 Conclusions
    6.3 Future Improvements and Research Areas
Acknowledgement
References
Publications



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