Advancing Industrial Inspection through Image Processing Techniques with OpenCV
Keywords:
machine vision, quality assurance, automated inspectionAbstract
Quality control in manufacturing has significantly progressed in recent years. However, traditional manual inspection methods often face challenges in terms of accuracy, consistency, and operational costs. To address these issues, OpenCV-based image processing technology has been adopted to enhance the efficiency of automated inspection systems. Studies have shown that integrating OpenCV into existing production lines significantly enhances defect detection accuracy while reducing inspection time. Applications in the electronics, automotive, and textile industries have shown that using computer vision algorithms for defect detection, size measurement, shape analysis, and color inspection improves
quality control precision and consistency. Additionally, OpenCV-based systems have proven effective in minimizing production errors, optimizing inspection efficiency, and reducing operational costs. These systems also operate reliably across diverse production environments, further enhancing industrial competitiveness. However, certain limitations such as sensitivity to lighting variations and complex object geometries still need further development. This approach lays a strong foundation for improving automated quality control systems, enabling a more accurate, cost-effective, and modern industrial inspection process.
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