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Laser & Optoelectronics Progress, Volume. 62, Issue 8, 0812002(2025)

Defect Detection of PCB Based on Lightweight ADS-YOLOv8n

Qitao Hu and Qijie Zou*
Author Affiliations
  • College of Information Engineering, Dalian University, Dalian 116622, Liaoning , China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (23)
    Equations (15)
    References (34)
    Cited By (2)
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    Paper Information
    Topics
    Figures & Tables(23)
    Network structure of YOLOv8n

    Fig. 1. Network structure of YOLOv8n

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    Network structure of ADS-YOLOv8n

    Fig. 2. Network structure of ADS-YOLOv8n

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    ADown downsampling structures diagram

    Fig. 3. ADown downsampling structures diagram

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    DTFM module structure diagram

    Fig. 4. DTFM module structure diagram

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    DySample module structure diagram. (a) Sampling based dynamic upsampling; (b) sampling point generator

    Fig. 5. DySample module structure diagram. (a) Sampling based dynamic upsampling; (b) sampling point generator

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    SC-bottleneck structure diagram

    Fig. 6. SC-bottleneck structure diagram

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    SCM structure diagram

    Fig. 7. SCM structure diagram

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    SCConv structure diagram

    Fig. 8. SCConv structure diagram

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    MLCA structure diagram

    Fig. 9. MLCA structure diagram

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    Types of PCB defects. (a) Missing hole; (b) mouse bite; (c) open circuit; (d) short circuit; (e) spur; (f) spurious copper

    Fig. 10. Types of PCB defects. (a) Missing hole; (b) mouse bite; (c) open circuit; (d) short circuit; (e) spur; (f) spurious copper

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    Comparison of recall and mAP@0.5 value curves. (a) Recall; (b) mAP@0.5

    Fig. 11. Comparison of recall and mAP@0.5 value curves. (a) Recall; (b) mAP@0.5

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    Comparison of class activation maps. (a) Original images; (b) YOLOv8n; (c) ADS-YOLOv8n

    Fig. 12. Comparison of class activation maps. (a) Original images; (b) YOLOv8n; (c) ADS-YOLOv8n

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    Comparison of defect detection results. (a)‒(f) YOLOv8n defect detection results; (g)‒(l) ADS-YOLOv8n defect detection results

    Fig. 13. Comparison of defect detection results. (a)‒(f) YOLOv8n defect detection results; (g)‒(l) ADS-YOLOv8n defect detection results

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    Comparison of detection results of NEU-DET data set

    Fig. 14. Comparison of detection results of NEU-DET data set

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    • Table 1. Number of PCB defect labels and pictures

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      Table 1. Number of PCB defect labels and pictures

      Defect typeOriginal imageEnhanced image
      Missing hole115460
      Mouse bite115460
      Open circuit116464
      Short circuit116464
      Spur115460
      Spurious copper116464
      Total6932772

    • Table 2. Ablation experiment of ADown module

      View table

      Table 2. Ablation experiment of ADown module

      NetworkRecall /%mAP@0.5 /%Parameter /106GFLOPs /109
      None91.4195.233.08.1
      Backbone92.9396.352.77.6
      Neck92.6296.032.97.9
      Backbone+Neck93.2696.572.67.4

    • Table 3. Ablation experiment

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      Table 3. Ablation experiment

      ModuleRecall /%mAP@0.5 /%Parameter /106GFLOPs /109
      None91.4195.233.08.1
      ADown93.2696.572.67.4
      DTFM92.2696.103.18.6
      SCM92.3596.252.98.0
      WIoUv392.1796.123.08.1
      ADown+DTFM95.6597.212.77.9
      ADown+SCM95.4197.132.47.3
      ADown+DTFM+SCM96.1597.872.57.8
      ADown+DTFM+SCM+WIoUv396.5898.432.57.8

    • Table 4. Comparison of validity experiment results

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      Table 4. Comparison of validity experiment results

      ModuleRecall /%mAP@0.5 /%
      NNI92.0395.88
      DySample92.2696.10

    • Table 5. Horizontal comparison of SCM

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      Table 5. Horizontal comparison of SCM

      ModulemAP@0.5 /%
      SC-bottleneck+CBAM95.92
      SC-bottleneck+ECA96.03
      SC-bottleneck+CA96.12
      SCM96.25

    • Table 6. Comparison of loss function

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      Table 6. Comparison of loss function

      LossmAP@0.5 /%
      CIoU97.87
      DIoU97.33
      EIoU97.46
      SIoU97.75
      WIoUv398.43

    • Table 7. Comparison of AP values of various defects

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      Table 7. Comparison of AP values of various defects

      ModelAP /%
      Missing holeMouse biteOpen circuitShort circuitSpurSpurious copper
      YOLOv8n98.6893.2594.4298.7091.8394.50
      ADS-YOLOv8n99.2597.9398.9199.4495.8399.22

    • Table 8. Comparative experiment of different algorithms

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      Table 8. Comparative experiment of different algorithms

      ModelmAP@0.5 /%Parameter /106GFLOPs /109
      Faster R-CNN87.20136.8‒
      SSD94.1212.338.8
      FCOS96.2732.1103.3
      Centernet95.3132.770.2
      YOLOv5n94.222.57.1
      YOLOv6n94.344.211.8
      YOLOv7-tiny95.166.013.2
      YOLOv8n95.233.08.1
      YOLOv8s97.3111.128.4
      Reference［10］99.147.27.2
      YOLOv5-TGs［28］98.206.714.4
      Reference［29］97.403.79.8
      SimAM-YOLO［30］98.397.219.2
      YOLO-P［31］98.805.2–
      FFS⁃YOLO［32］99.226.1–
      YOLOv5-HSTE［33］98.707.848.7
      YOLOv8-PCB［34］98.372.611.7
      ADS-YOLOv8n98.432.57.8

    • Table 9. Comparison of experimental results on generalization performance

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      Table 9. Comparison of experimental results on generalization performance

      ModelRecall /%mAP@0.5 /%Parameter /106GFLOPs /109
      YOLOv8n71.3270.633.08.1
      ADS-YOLOv8n74.8773.262.57.8
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    Qitao Hu, Qijie Zou. Defect Detection of PCB Based on Lightweight ADS-YOLOv8n[J]. Laser & Optoelectronics Progress, 2025, 62(8): 0812002

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    Paper Information

    Category: Instrumentation, Measurement and Metrology

    Received: Aug. 28, 2024

    Accepted: Oct. 8, 2024

    Published Online: Apr. 3, 2025

    The Author Email: Qijie Zou (jessie_zou_zou@163.com)

    DOI:10.3788/LOP241923

    CSTR:32186.14.LOP241923

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology