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PCB Electronic Component Soldering Defect Detection Using YOLO11 Improved by Retention Block and Neck Structure.

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Summary
This summary is machine-generated.

This study enhances the YOLO11n model for printed circuit board (PCB) defect detection. The improved model achieves higher accuracy in identifying surface mount electronic component welding defects.

Keywords:
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Area of Science:

  • Electronics Manufacturing
  • Computer Vision
  • Artificial Intelligence

Background:

  • Printed circuit board (PCB) assembly is crucial in electronics manufacturing.
  • Defect detection in PCB assembly is vital for industrial quality control.
  • Existing target detection algorithms (two-stage and single-stage) have limitations in parameter count, runtime, or accuracy.

Purpose of the Study:

  • To improve the accuracy and efficiency of PCB defect detection.
  • To address the limitations of traditional and single-stage target detection models.
  • To innovate and modify the YOLO11n model for enhanced performance.

Main Methods:

  • Modified the YOLO11n model by incorporating a Retention Block (RetBlock) into the C3K2 module, creating the RetC3K2 module.
  • Enhanced the neck structure by fusing it with a Multi-Branch Auxiliary Feature Pyramid Network (MAFPN).
  • Developed a multi-branch auxiliary neck network to improve multi-scale feature fusion and gradient information conveyance.

Main Results:

  • The improved YOLO11n model demonstrated a significant increase in detection precision.
  • Achieved an improvement of 0.023 (2.5%) in mAP50 and 0.026 (2.8%) in mAP75 compared to the primitive model.
  • The proposed modifications effectively addressed the limitations of the original YOLO11n architecture.

Conclusions:

  • The enhanced YOLO11n model offers superior performance for PCB defect detection.
  • The integration of RetBlock and MAFPN significantly boosts detection accuracy and efficiency.
  • This approach provides a more effective solution for automated quality control in electronics assembly.