Papers
2
Total Citations
12
H-Index
2
About
Jordan Tay is a rising researcher at the forefront of applying computer vision to critical aerospace safety challenges. His primary research focuses on automated defect detection for aircraft skin surfaces, a domain where traditional manual inspections are both time-consuming and prone to error. Tay’s major contribution lies in systematically evaluating and comparing state-of-the-art object detection architectures—specifically YOLOv9 and RT-DETR—for identifying surface anomalies. His comprehensive survey work, which has garnered over a dozen citations within its first year of publication, provides a vital benchmark for the field, demonstrating how deep learning models can achieve reliable, real-time inspection. By bridging the gap between advanced computer vision algorithms and practical aviation maintenance needs, Tay’s research offers a clear pathway toward safer, more efficient aircraft operations. His work not only highlights the transformative potential of automation in quality control but also establishes a foundational reference for future studies in non-destructive testing and industrial AI.
Research Focus
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Top Papers
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