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This study introduces an improved YOLOv11 model for enhanced surface defect detection in electronic products. The new method significantly boosts precision and recall rates, offering a more reliable automated solution for manufacturing quality control.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Manufacturing Technology

Background:

  • Traditional manual inspection methods for electronic products are limited by operator variability and struggle with increasing demands for efficiency and precision.
  • Deep learning, particularly object detection, shows promise for automating quality control in manufacturing.
  • Existing YOLO models face challenges in detecting small defects against complex backgrounds.

Purpose of the Study:

  • To develop an improved YOLOv11-based deep learning model for accurate surface defect detection in electronic products.
  • To address the limitations of current YOLO models in identifying small defects and complex backgrounds.
  • To enhance the precision, recall rate, and detection speed of automated defect detection systems.

Main Methods:

  • An improved YOLOv11 architecture was proposed, incorporating the MD-C2F module, DualConv module, and Inner_MPDIoU loss function.
  • The model was trained and evaluated for surface defect detection in electronic products.
  • Performance was benchmarked against previous YOLO versions (YOLOv7, YOLOv8, YOLOv9) and on the PKU-Market-PCB dataset.

Main Results:

  • The improved YOLOv11 model achieved a precision of 93.1% (up from 90.9%) and a recall rate of 84.6% (up from 77.0%).
  • mAP50 increased by 4.6% to 88.6%, outperforming other YOLO versions in detecting various defects like resistors, LED lights, and capacitors.
  • Generalization tests on the PKU-Market-PCB dataset showed improved accuracy (94.6%), recall (91.2%), and mAP50 (95.4%).

Conclusions:

  • The proposed YOLOv11 model effectively overcomes challenges in detecting small defects in complex backgrounds and varying scales.
  • Significant enhancements in detection accuracy, recall, and generalization ability were demonstrated.
  • The improved YOLOv11 offers a robust and automated solution for defect detection in electronic product manufacturing.