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5G環境下基于深度學習的云化PLC物料識別與定位系統

付美霞 王健全 王曲 孫雷 馬彰超 張超一 管婉青 李衛

付美霞, 王健全, 王曲, 孫雷, 馬彰超, 張超一, 管婉青, 李衛. 5G環境下基于深度學習的云化PLC物料識別與定位系統[J]. 工程科學學報, 2023, 45(10): 1666-1673. doi: 10.13374/j.issn2095-9389.2022.12.18.001
引用本文: 付美霞, 王健全, 王曲, 孫雷, 馬彰超, 張超一, 管婉青, 李衛. 5G環境下基于深度學習的云化PLC物料識別與定位系統[J]. 工程科學學報, 2023, 45(10): 1666-1673. doi: 10.13374/j.issn2095-9389.2022.12.18.001
FU Meixia, WANG Jianquan, WANG Qu, SUN Lei, MA Zhangchao, ZHANG Chaoyi, GUAN Wanqing, LI Wei. Material recognition and location system with cloud programmable logic controller based on deep learning in 5G environment[J]. Chinese Journal of Engineering, 2023, 45(10): 1666-1673. doi: 10.13374/j.issn2095-9389.2022.12.18.001
Citation: FU Meixia, WANG Jianquan, WANG Qu, SUN Lei, MA Zhangchao, ZHANG Chaoyi, GUAN Wanqing, LI Wei. Material recognition and location system with cloud programmable logic controller based on deep learning in 5G environment[J]. Chinese Journal of Engineering, 2023, 45(10): 1666-1673. doi: 10.13374/j.issn2095-9389.2022.12.18.001

5G環境下基于深度學習的云化PLC物料識別與定位系統

doi: 10.13374/j.issn2095-9389.2022.12.18.001
基金項目: 國家重點研發計劃資助項目(2020YFB1708800);廣東省重點研究與開發計劃資助項目(2020B010113007);中央高校基本科研業務費專項資金資助項目(FRF-IDRY-21-005);廣東省基礎與應用基礎研究基金聯合基金資助項目(2021A1515110577);中央高校基礎研究基金資助項目(FRF-MP-20-37);中國博士后科學基金資助項目(2021M700385)
詳細信息
    通訊作者:

    E-mail: wangjianquan@ustb.edu.cn

  • 中圖分類號: TG142.71

Material recognition and location system with cloud programmable logic controller based on deep learning in 5G environment

More Information
  • 摘要: 為了解決智能制造領域中云化控制與視覺分選應用相結合的問題,提出了基于深度學習的云化可編程邏輯控制器(Programmable logic controller,PLC)物料識別與定位系統,并在端到端5G與時間敏感網絡(Time sensitive networking,TSN)傳輸網絡環境下,實現了對云化PLC架構和控制功能有效性的驗證。首先,將傳統PLC系統控制功能容器虛擬化,實現PLC的本地和云端自由部署;其次,在云端設計人工智能學習平臺,采用基于You only look once v5 (YOLOv5)的目標檢測算法實現物料的定位和分類,獲取目標的像素坐標和類別信息;然后,利用相機標定方法把像素坐標轉換成物理世界坐標,并將目標類別、坐標、時間戳信息傳輸到云化PLC;最后,在5G和TSN融合網絡環境下,實現云化PLC對天車設備的實時控制與復雜計算功能整合。結果表明,該系統能夠有效的對多天車進行協同控制,物料定位均值平均精度(Mean average precision,mAP)達到99.65%,分選準確率達到96.67%,平均消耗時間225.99 s,滿足工業低時延、高精度的視覺分選需求。

     

  • 圖  1  云化PLC系統架構

    Figure  1.  Architecture of cloud based-PLC system architecture

    圖  2  基于YOLOv5的物料識別與檢測網絡

    Figure  2.  Material recognition and detection network based on YOLOv5

    圖  3  相機校準示意圖

    Figure  3.  Mathematical model of camera calibration

    圖  4  試驗設備

    Figure  4.  Main experimental devices

    圖  5  象棋棋子檢測效果展示

    Figure  5.  Experimental results of material detection based on YOLOv5

    圖  6  多天車物料分揀效果展示

    Figure  6.  Performance of the multi-crane visual sorting system

    表  1  基于物料檢測的實驗結果

    Table  1.   Experimental results of material detection

    ModelDatasetNumberPrecision/%Recall rate/%mAP/%Time/ms
    Cascade-RCNN[23]Training set14110010010032
    Testing set30100100100
    Validating set30100100100
    SSD[22]Training set14110010010024
    Testing set30100100100
    Validating set30100100100
    YOLOv5[24]Training set14199.9910099.6012
    Testing set3010010099.59
    Validating set3010010099.65
    下載: 導出CSV

    表  2  多天車視覺分選系統的消耗時間和準確率

    Table  2.   Consuming time and accuracy of the multi-crane visual sorting system

    No.Conveyor belt
    speed/(cm·s–1)
    Consuming
    time/s
    Missed detection of red
    chesses pieces
    Missed detection of
    black chesses pieces
    All accuracy/%
    12.2327.075/05/0100
    228.555/05/0100
    326.795/05/0100
    42.6125.685/05/0100
    525.765/05/0100
    625.405/05/0100
    73.0123.385/05/0100
    830.335/15/090
    927.925/05/190
    103.3923.605/25/080
    1124.165/05/0100
    1223.355/05/0100
    Average25.9960/360/196.67
    下載: 導出CSV
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    259luxu-164
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  • 收稿日期:  2022-12-18
  • 網絡出版日期:  2023-02-27
  • 刊出日期:  2023-10-25

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