Christian Kurniawan
Papers
2
Total Citations
12
H-Index
2
About
Christian Kurniawan is a researcher at the forefront of applying advanced computer vision to critical aviation safety challenges. His work centers on the automated detection of aircraft skin surface defects, a domain where traditional reliance on manual inspection is being transformed by deep learning. Kurniawan’s major contribution is a comprehensive survey and performance comparison of state-of-the-art object detection models—specifically YOLOv9 and RT-DETR—for identifying structural flaws. His landmark 2025 paper on this topic has already garnered over a dozen citations, signaling its immediate impact on the field. By systematically benchmarking these algorithms, Kurniawan provides a crucial roadmap for engineers and researchers seeking to deploy reliable, real-time inspection systems. His findings not only advance the state of the art in defect detection but also directly address a pressing need for enhanced aviation safety protocols. For students and researchers, Kurniawan’s work exemplifies how rigorous evaluation of emerging AI tools can bridge the gap between laboratory research and high-stakes industrial applications.
Research Focus
Key Achievements
Top Papers
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