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<p>This work presents a novel end-to-end approach to early defect detection in thermo-insulating panels, widely used in the construction sector. Unlike existing studies that focus on isolated aspects, our contribution combines: (i) a composable and interoperable software platform designed to support data augmentation, segmentation, and classification; (ii) a multi-technique synthetic image generation strategy that overcomes the limitations of scarce and unbalanced datasets; and (iii) a hybrid segmentation and classification pipeline that extends traditional U-Net architectures with post-segmentation polygonal analysis for robust defect identification. The proposed solution is validated on the GAMHE 5.0 Industry 5.0 pilot line, demonstrating not only improvements in detection accuracy and control time but also its feasibility in human-in-the-loop, near-production environments, a gap rarely addressed in the literature. These results confirm that the approach goes beyond a mere consolidation of existing methods, providing instead a scalable, human-centric, and sustainable framework for Zero-Defect Manufacturing.</p>

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Early defect detection in thermo-insulating panels: A case study on quality inspection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月4日

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  • Rubén Alonso,
  • Fernando Castaño,
  • Yarens J. Cruz,
  • Alberto Villalonga,
  • Rodolfo E. Haber

摘要

This work presents a novel end-to-end approach to early defect detection in thermo-insulating panels, widely used in the construction sector. Unlike existing studies that focus on isolated aspects, our contribution combines: (i) a composable and interoperable software platform designed to support data augmentation, segmentation, and classification; (ii) a multi-technique synthetic image generation strategy that overcomes the limitations of scarce and unbalanced datasets; and (iii) a hybrid segmentation and classification pipeline that extends traditional U-Net architectures with post-segmentation polygonal analysis for robust defect identification. The proposed solution is validated on the GAMHE 5.0 Industry 5.0 pilot line, demonstrating not only improvements in detection accuracy and control time but also its feasibility in human-in-the-loop, near-production environments, a gap rarely addressed in the literature. These results confirm that the approach goes beyond a mere consolidation of existing methods, providing instead a scalable, human-centric, and sustainable framework for Zero-Defect Manufacturing.