Haiwen Dai
Papers
1
Total Citations
18
H-Index
1
About
Haiwen Dai is a researcher at the intersection of computer vision, deep learning, and non-destructive inspection, with a focus on automated analysis of 3D X-ray imagery. His most cited work, "Automated Attribute Measurements of Buried Package Features in 3D X-ray Images using Deep Learning" (2021, 18 citations), pioneers the application of state-of-the-art deep learning models for 3D object detection and segmentation of buried structures, such as through-hole vias in electronic packages. By adapting advanced techniques from robotics and medical imaging, Dai enables precise, automated measurement of hidden features—a critical advancement for quality control in manufacturing and security screening. His contributions demonstrate how deep learning can transform traditional inspection workflows, reducing manual effort and improving accuracy. With a growing citation impact, Dai’s work bridges applied AI and industrial imaging, offering scalable solutions for real-world challenges. His research not only advances non-destructive evaluation but also inspires further integration of machine learning into materials analysis and structural health monitoring.
Research Focus
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Top Papers
- 1