Authors:
Beatriz Coutinho
1
;
Tomás Martins
2
;
Eliseu Pereira
1
and
Gil Gonçalves
1
Affiliations:
1
SYSTEC ARISE, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
;
2
Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Keyword(s):
Computer Vision, Defect Detection, Quality Monitoring, Non-Destructive Inspection, Zero Defects Manufacturing.
Abstract:
In the wood panel manufacturing industry, maintaining high product quality is critical to ensure customer satisfaction and minimize resource waste. Manual quality inspection methods are often inconsistent, increasing the risk of defective panels reaching the market. This paper introduces an automated visual inspection system for decorative wood panels, aligned with the Detection strategy of the Zero Defects Manufacturing (ZDM) framework. Designed for real-time deployment on an NVIDIA Jetson Nano, the system inspects panels independently without disrupting the production line and visually highlights detected defects for operator review. Two implementation approaches were explored and compared: a traditional computer vision pipeline and a deep learning-based solution. Due to the limited availability of real-world defect images, a synthetic dataset was created using patch blending, tiling, and diverse augmentations to improve the model’s generalization across spatial variations. Experim
ental evaluation with static images and live video showed that while traditional methods achieve moderate detection accuracy, they fail under varying lighting and camera angles. In contrast, the YOLO-based approach delivered robust segmentation and superior defect detection, even under challenging conditions. These results highlight the system’s potential to assist operators during manual inspections and contribute to practical advances to achieve ZDM.
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