Jetanat Datephanyawat
Papers
2
Total Citations
12
H-Index
2
About
Jetanat Datephanyawat is a rising researcher at the forefront of applying computer vision to aerospace safety, with a primary focus on automated aircraft skin defect detection. Their major contribution lies in bridging the gap between traditional manual inspection methods and modern deep learning automation. In their seminal 2025 survey, Datephanyawat provided a comprehensive review of state-of-the-art computer vision algorithms for identifying surface defects critical to aviation safety. The work notably includes a rigorous comparative analysis of two leading object detection models—YOLOv9 and RT-DETR—evaluating their performance, accuracy, and real-world applicability for aircraft maintenance. This study has quickly garnered attention, accumulating 9 and 3 citations in separate entries, reflecting its timely relevance and practical impact. By systematically benchmarking these advanced architectures, Datephanyawat has established a foundational reference for researchers and engineers seeking to automate visual inspections, potentially reducing human error and improving flight safety. Their work stands as a key resource for students and professionals exploring the intersection of computer vision, deep learning, and aviation engineering.
Research Focus
Key Achievements
Top Papers
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