English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 62565/95219 (66%)
造訪人次 : 2527969      線上人數 : 232
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/124252

    題名: Efficient convolutional neural networks on Raspberry Pi for image classification
    作者: Chiang, Jen‑shiun
    關鍵詞: Edge computing platform;Image classification;Convolutional neural network;Model acceleration;Model compression;Raspberry Pi
    日期: 2023-02-18
    上傳時間: 2023-07-19 12:05:23 (UTC+8)
    出版者: Springer Journal of Real-Time Image Processing
    摘要: With the good performance of deep learning in the field of computer vision (CV), the convolutional neural network (CNN) architectures have become main backbones of image recognition tasks. With the widespread use of mobile devices, neural network models based on platforms with low computing power are gradually being paid attention. However, due to the limitation of computing power, deep learning algorithms are usually not available on mobile devices. This paper proposes a lightweight convolutional neural network TripleNet, which can operate easily on Raspberry Pi. Adopted from the concept of block connections in ThreshNet, the newly proposed network model compresses and accelerates the network model, reduces the amount of parameters of the network, and shortens the inference time of each image while ensuring the accuracy. Our proposed TripleNet and other State-of-the-Art (SOTA) neural networks perform image classification experiments with the CIFAR-10 and SVHN datasets on Raspberry Pi. The experimental results show that, compared with GhostNet, MobileNet, ThreshNet, EfficientNet, and HarDNet, the inference time of TripleNet per image is shortened by 15%, 16%, 17%, 24%, and 30%, respectively. The detail codes of this work are available at https:// github. com/ Ruiya ngJu/ Tripl eNet.
    關聯: Journal of Real-Time Image Processing 20, 21
    DOI: 10.1007/s11554-023-01271-1
    顯示於類別:[電機工程學系暨研究所] 期刊論文


    檔案 描述 大小格式瀏覽次數



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋