Early defect detection in thermo-insulating panels: A case study on quality inspection
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 da...
| Authors: | , , , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2025 |
| Country: | España |
| Institution: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repository: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/417793 |
| Online Access: | http://hdl.handle.net/10261/417793 |
| Access Level: | Open access |
| Keyword: | Industry 5.0 Quality control Early defect detection Sustainable manufacturing Pilot lines |
| Summary: | 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 humanin-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. |
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