A Deep Learning-Based Object Representation Algorithm for Smart Retail Management

被引:0
|
作者
Liu B. [1 ]
机构
[1] Haojing College of Shaanxi University of Science and Technology, Shaanxi, Xi’an
关键词
Computer vision; Deep learning; Object representation; Smart retail management; YOLOv7;
D O I
10.1007/s40031-024-01051-w
中图分类号
学科分类号
摘要
This study underscores the vital role of object representation and detection in smart retail management systems for optimizing customer experiences and operational efficiency. The literature review reveals a preference for deep learning techniques, citing their superior accuracy compared to traditional methods. While acknowledging the challenges of achieving high accuracy and low computation costs simultaneously in deep learning-based object representation, the paper proposes a solution using the YOLOv7 framework. In order to navigate the ever-changing landscape of smart retail technologies, the study clarifies the potential scalability and flexibility of deep learning approaches. The method employs a custom dataset, and experimental results demonstrate the model’s efficacy, showcasing accurate results and enhanced performance in various experiments and analyses. © The Institution of Engineers (India) 2024.
引用
收藏
页码:1121 / 1128
页数:7
相关论文
共 50 条
  • [31] Deep Learning-Based Thermal Image Reconstruction and Object Detection
    Batchuluun, Ganbayar
    Kang, Jin Kyu
    Nguyen, Dat Tien
    Pham, Tuyen Danh
    Arsalan, Muhammad
    Park, Kang Ryoung
    IEEE ACCESS, 2021, 9 : 5951 - 5971
  • [32] Deep Learning-Based Object Detection Improvement for Tomato Disease
    Zhang, Yang
    Song, Chenglong
    Zhang, Dongwen
    IEEE ACCESS, 2020, 8 : 56607 - 56614
  • [33] Deep Learning-Based Object Detection in Diverse Weather Conditions
    Ravinder, M.
    Jaiswal, Arunima
    Gulati, Shivani
    INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES, 2022, 18 (01)
  • [34] Deep Learning-Based Feature Representation for AD/MCI Classification
    Suk, Heung-Il
    Shen, Dinggang
    MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2013, PT II, 2013, 8150 : 583 - 590
  • [35] Deep Residual Learning-based Reconstruction of Stacked Autoencoder Representation
    Li, Honggui
    Trocan, Maria
    2018 25TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS AND SYSTEMS (ICECS), 2018, : 655 - 656
  • [36] Smart Grid Energy Management Using RNN-LSTM: A Deep Learning-based Approach
    Kaur, Devinder
    Kumar, Rahul
    Kumar, Neeraj
    Guizani, Mohsen
    2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2019,
  • [37] Deep learning-based methodology for vulnerability detection in smart contracts
    Wang, Zhibo
    Guoming, Liu
    Xu, Hongzhen
    You, Shengyu
    Ma, Han
    Wang, Hongling
    PEERJ COMPUTER SCIENCE, 2024, 10
  • [38] Deep learning-based energy inefficiency detection in the smart buildings
    Huang, Jueru
    Koroteev, Dmitry D.
    Rynkovskaya, Marina
    SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS, 2023, 40
  • [39] Deep Learning-based Automatic Optimization of Design Smart Home
    Wang Z.
    Wang D.
    Computer-Aided Design and Applications, 2024, 21 (S18): : 96 - 113
  • [40] Deep learning-based smart vision for building and construction application
    Yue, Li
    ADVANCES IN CONCRETE CONSTRUCTION, 2024, 18 (01) : 65 - 74