Journal of Optoelectronics · Laser, Volume. 35, Issue 5, 506(2024)

Research on defect detection of lightweight PCB based on dual channel attention

PENG Hui, ZHOU Bowen*, OUYANG Wanqing, and LUO Jianghong
Author Affiliations
  • [in Chinese]
  • show less
    References(14)

    [1] [1] GIRSHICK R.Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision (ICCV),December 7-13,2015,Santiago,Chile.New York:IEEE,2015:1440-1448.

    [2] [2] REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149.

    [3] [3] LIU X,HU J,WANG H,et al.Gaussian-IoU loss:Better learning for bounding box regression on PCB component detection[J].Expert Systems with Applications,2022,190(C):116178.

    [4] [4] LI C J,QU Z,WANG S Y.et al.Amethod of defect detection for focal hard samples PCB based on extended FPN Model[J].IEEE Transactions on Components,Packaging and Manufacturing Technology,2022,12(2):217-227.

    [5] [5] ZHANG H,JIANG L,LI C.CS-ResNet:Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection[J].Expert Systems with Applications,2021,185(C):115673.

    [6] [6] ZHANG H,ZU K,LU J,et al.EPSANet:An efficient pyramid split attention block on convolutional neural network[EB/OL].(2021-07-22)[2023-01-04].https://arxiv.org/abs/2105.14447.

    [7] [7] LIAN J,WANG L,LIU T,et al.Automatic visual inspectionfor printed circuit board via novel mask R-CNN in smart city applications[J].Sustainable Energy Technologies and Assessments,2021,44(2):101032.

    [8] [8] ZHANG Q,LIU H.Multi-scale defect detection of printed circuitboard based on feature pyramid network[C]//2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA),June 28-30,2021,Dalian,China.New York:IEEE,2021:911-914.

    [9] [9] SOLORZANO C,TSAI D M.Environment-adaptable printed-circuit board positioning using deep reinforcement learnin[J].IEEE Transactions on Components,Packaging and Manufacturing Technology,2022,12(2):382-390.

    [10] [10] LU Y,SUN C,LI X,et al.Defect detection of integrated circuit based on YOLOv5[C]//2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI),May 6-8, 2022,Beijing,China.New York:IEEE,2022:165-170.

    [11] [11] ZHU X,LYU S,WANG X,et al.TPH-YOLOv5:Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios[C]//IEEE/CVF International Conference on Computer Vision Workshops (ICCVW),October 11-17,2021,Montreal,BC,Canada.New York: IEEE,2021:2778-2788.

    [12] [12] LI M,YAO N,LIU S,et al.Multisensor image fusion for automated detection of defects in printed circuit boards[J].IEEE Sensors Joumal,2021,21(20):23390-23399.

    [13] [13] WU L,ZHANG L,ZHOU Q.Printed circuit board quality detection method integrating lightweight network and dual attention mechanism[J].IEEE Access,2022,10:87617-87629.

    [14] [14] ZHANG H,WU C R,ZHANG Z Y,et al.ResNeSt:Split-attention networks[EB/OL](2020-12-30)[2023-01-04].https://arxiv.org/abs/2004.08955.

    Tools

    Get Citation

    Copy Citation Text

    PENG Hui, ZHOU Bowen, OUYANG Wanqing, LUO Jianghong. Research on defect detection of lightweight PCB based on dual channel attention[J]. Journal of Optoelectronics · Laser, 2024, 35(5): 506

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Received: Jan. 4, 2023

    Accepted: --

    Published Online: Sep. 24, 2024

    The Author Email: ZHOU Bowen (bowenzhou@163.com)

    DOI:10.16136/j.joel.2024.05.0707

    Topics