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Author

Adem Dilbaz

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3 papers indexed here

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Open access Oct 2026

CAEFRER-YOLO11: An Adaptive Feature Refinement-Based Hybrid Model and Dataset for Weld Defect Detection

Weld quality inspection is essential for ensuring structural integrity, product reliability, and manufacturing safety in industrial applications. However, conventional visual inspection methods can be time-consuming, operator-dependent, and prone to overlooking subtle weld defects. YOLO11-based detectors employ feature...

Adem Dilbaz, I. Ozkan · 0 citations
Open access Sep 2026

YOLOv10 and RT-DETR for Weld Defect Detection: Experimental Evaluation

A systematic evaluation of YOLOv10 and RT-DETR architectures for weld defect detection using the publicly available Weld Defects (WD) dataset provides a comprehensive comparison of CNN- and Transformer-based detectors and offers practical guidance for selecting suitable deep learning models for reliable automated weld...

Adem Dilbaz · 1 citation
Open access Aug 2026

YOLO12 and YOLO13 for Steel Surface Defect Detection: Experimental Evaluation

Intelligent inspection and defect detection of steel surfaces are essential for ensuring product quality, minimizing production losses, and supporting quality control in modern manufacturing systems. In particular, the identification of defects on steel plates is an important step, as surface imperfections can signific...

Adem Dilbaz · 0 citations

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