Best Paper Award
Md Rashidunnabi — University of Beira Interior
Md Rashidunnabi of the University of Beira Interior, Portugal, is recognized with the Best Paper Award for research in Computer Science. His recognized paper, “LUSITANOv2: A Real-World Dataset for Fabric Defect Detection in Active Textile Production”, focuses on a real-world dataset for fabric-defect detection collected in an active textile production environment. The research connects computer vision, machine learning, industrial inspection, and intelligent manufacturing.
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Abstract
This article recognizes Md Rashidunnabi with the Best Paper Award for research in Computer Science. His recognized work, “LUSITANOv2: A Real-World Dataset for Fabric Defect Detection in Active Textile Production”, introduces a high-resolution fabric-defect dataset collected at an active textile inspection station using an industrial line-scan camera and directional illumination. The dataset contains 25,120 native images, including both defect-containing and defect-free fabric, together with 18,557 class-agnostic bounding boxes.[1]
Keywords
Best Paper Award, Md Rashidunnabi, Computer Science, LUSITANOv2, Fabric Defect Detection, Textile Production, Computer Vision, Machine Learning, Artificial Intelligence, Industrial Inspection, Automated Quality Control, Deep Learning, Smart Manufacturing, Intelligent Manufacturing, Textile Industry, Industrial AI, Visual Inspection, Manufacturing Automation.[1]
Introduction
Automated fabric-defect detection is an important application of computer vision and artificial intelligence in modern textile manufacturing. Reliable inspection can be challenging because production-line imagery may differ from controlled laboratory samples in textile appearance, illumination, imaging conditions, and defect characteristics.[1]
Research Profile
Md Rashidunnabi is affiliated with the University of Beira Interior in Portugal and works within the field of Computer Science. The supplied Google Scholar profile records 13 documents, 29 citations, and an h-index of 3.[2]
Research Contributions
The recognized paper contributes a real-world dataset for fabric-defect detection collected directly from an active textile production environment. LUSITANOv2 contains 25,120 native images and 18,557 localized defect instances, providing research material for computer-vision applications. The dataset supports supervised object detection as well as one-class anomaly-detection research. The paper evaluates YOLOv12n, Faster R-CNN, and RT-DETR-L for supervised detection and nine one-class anomaly-detection methods for anomaly-based inspection.[1]
Publications
The principal publication associated with this award profile is “LUSITANOv2: A Real-World Dataset for Fabric Defect Detection in Active Textile Production”, authored by Rui Carrilho, Md Rashidunnabi, and Hugo Proença. The article was published in Electronics, Volume 15, Issue 19, Article 4403, on 24 September 2026. The article DOI is 10.3390/electronics15194403.[1]
Research Impact
LUSITANOv2 provides research material for automated visual inspection of textile products. Its real-world production imagery enables researchers to investigate computer-vision systems under operational conditions rather than only controlled laboratory environments. The dataset is relevant to defect localization, anomaly detection, deep learning, domain generalization, and industrial computer vision. The benchmark experiments provide baseline information that can support future evaluation of fabric-inspection methods.[1]
Award Recognition
Md Rashidunnabi is recognized with the Best Paper Award in Computer Science for the research contribution represented by “LUSITANOv2: A Real-World Dataset for Fabric Defect Detection in Active Textile Production.” The research addresses real-world fabric-defect detection and provides a dataset for evaluating computer-vision and artificial-intelligence methods in active textile production.[1]
Conclusion
Md Rashidunnabi of the University of Beira Interior, Portugal, is recognized with the Best Paper Award for research in Computer Science. His recognized paper presents LUSITANOv2, a real-world dataset for fabric-defect detection in active textile production.[1]
External Links
References
- Carrilho, R., Rashidunnabi, M., & Proença, H. (2026).
LUSITANOv2: A Real-World Dataset for Fabric Defect Detection in Active Textile Production.
Electronics, 15(19), 4403.
https://doi.org/10.3390/electronics15194403
- Google Scholar. (n.d.). Google Scholar Profile: Md Rashidunnabi.
https://scholar.google.com/citations?user=0_6ryVoAAAAJ&hl=en
- Best Paper Awards. (n.d.). International Research Excellence and Best Paper Awards.
https://bestpaperawards.com/