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Multi-Level Scale Attention Fusion Network for Adhesive Spots Segmentation in Microlens Packaging.

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Summary

A new network, MLSAFNet, accurately detects adhesive spots on laser collimating lenses. This method improves precision inspection for high-power laser applications.

Keywords:
adhesive spots segmentationmulti-level attentionmulti-scale channel-guided

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Area of Science:

  • Optics and Photonics
  • Machine Learning
  • Quality Control

Background:

  • High-power lasers require precise collimating lens packages.
  • Accurate inspection of adhesive points is crucial for lens quality.
  • Existing methods lack the required precision for adhesive spot detection.

Purpose of the Study:

  • To develop a highly accurate and robust method for detecting adhesive spots on laser collimating lenses.
  • To introduce the Multi-Level Scale Attention Fusion Network (MLSAFNet) for this task.
  • To create and standardize a novel dataset for laser lens adhesive spots (LLAS).

Main Methods:

  • Proposed the Multi-Level Scale Attention Fusion Network (MLSAFNet).
  • Integrated a Multi-Level Attention Module (MLAM) and a Multi-Scale Channel-Guided Module (MSCGM).
  • Developed the Laser Lens Adhesive Spots (LLAS) dataset using automated equipment and pixel-level standardization.

Main Results:

  • MLSAFNet achieved a mean intersection over union (mIoU) of 91.15%.
  • Maximum localization error was 21.83 μm, and area measurement error was 0.003 mm².
  • Performance surpassed other target detection methods.

Conclusions:

  • MLSAFNet offers a significant advancement in adhesive spot detection for laser lens packages.
  • The developed LLAS dataset and MLSAFNet contribute to improved quality control in high-power laser manufacturing.
  • The method demonstrates high accuracy and robustness, meeting the demands of precision inspection.