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With the emergence of machine vision in defect detection system, it greatly enhanced the accuracy, reliability, and quality of production in industries by saving time and cost. In this study, the image processing and semantic segmentation analysis played vital roles in extraction of defects information on optical filter surface with the help of Halcon software. The study focused on identifying, localizing, and characterizing different types of defects on several shapes of optical filter surfaces with potential applications in optical industry. The experimental results showing overall pixel accuracy of 98.8% indicated that proposed deep learning model is highly efficient and accurate for real-time batch inspection than manual inspection in the optical industries.

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Machine Vision Defect Detection System Designed with Halcon

  • 会议论文

  • 发表日期：

    2025年

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  • Mir Mehraj Ahmad,
  • Lyuchao Liao,
  • Jishi Zheng,
  • Ali Shan,
  • Jindun Zeng

摘要

With the emergence of machine vision in defect detection system, it greatly enhanced the accuracy, reliability, and quality of production in industries by saving time and cost. In this study, the image processing and semantic segmentation analysis played vital roles in extraction of defects information on optical filter surface with the help of Halcon software. The study focused on identifying, localizing, and characterizing different types of defects on several shapes of optical filter surfaces with potential applications in optical industry. The experimental results showing overall pixel accuracy of 98.8% indicated that proposed deep learning model is highly efficient and accurate for real-time batch inspection than manual inspection in the optical industries.