@ARTICLE{Bumbálek_R._Detection_2026,
 author={Bumbálek, R. and Zoubek, T. and Majerník, J. and de Dieu Marcel Ufitikirezi, J. and Umurungi, S.N. and Šramhauser, K. and Špalek, F.},
 volume={vol. 26},
 number={No 1},
 pages={165-176},
 journal={Archives of Foundry Engineering},
 howpublished={online},
 year={2026},
 publisher={The Katowice Branch of the Polish Academy of Sciences},
 abstract={The early detection and classification of surface defects in metallic materials is essential for ensuring product quality and reliability in industrial production. In this study, we evaluated and compared five recent versions of the YOLO convolutional neural network architecture (YOLOv8x, YOLOv9e, YOLOv10x, YOLOv11x, and YOLOv12x) applied to five publicly available datasets specialized in metallic defect detection (AlcastXray, Castings, GC10-DET, NEU-SEG, and Severstal SDD). All models were trained under uniform conditions and assessed using Precision, Recall, mAP50, mAP50–95, and deployment-oriented efficiency metrics, including inference speed, throughput, GPU memory usage, and power draw. The results show that YOLOv9e consistently achieved the highest detection accuracy (mAP50 up to 0.982), while YOLOv8x provided the fastest inference (>47 FPS), making it suitable for real-time applications. YOLOv10x, the most lightweight model, delivered a favorable trade-off between accuracy and computational efficiency, suggesting strong potential for edge deployment. These findings provide practical insights for selecting YOLO-based architectures according to specific industrial requirements in metallic surface defect inspection.},
 title={Detection of Surface Defects in Metallic Materials Using Convolutional Neural Networks with YOLO Architecture},
 type={Article},
 URL={http://czasopisma.pan.pl/Content/138676/AFE%201_2026_20-Final%20version.pdf},
 doi={10.24425/afe.2026.157983},
 keywords={Computer vision, Defect detection, CNN, Industry 4.0, NDT},
}