Hee‐Mun Park
Papers
1
Total Citations
9
H-Index
1
About
Hee-Mun Park is a researcher at the forefront of industrial artificial intelligence, specializing in deep learning-based object detection and manufacturing quality control. Park’s most notable contribution is the development of an optimized YOLO network that employs a single circular bounding box for detecting defective cigarettes, a breakthrough that bridges advanced computer vision with real-world production line challenges. This work, published in 2023 and already garnering 9 citations, demonstrates Park’s ability to tailor state-of-the-art AI models to specific industrial needs, improving both accuracy and efficiency in automated inspection systems. By integrating computing technology, robotics, and IoT with deep learning, Park addresses critical gaps in manufacturing quality assurance, offering scalable solutions that reduce waste and enhance product reliability. Park’s research not only advances the practical application of object detection in industry but also sets a precedent for future innovations in smart manufacturing. With a growing citation footprint and a focus on translating complex algorithms into tangible industrial outcomes, Hee-Mun Park is a rising voice in the intersection of AI and production engineering, inspiring students and researchers to explore how deep learning can transform traditional manufacturing processes.
Research Focus
Key Achievements
Top Papers
- 1