Sanghoon Kim

Hankyong National University, Konkuk University Medical Center

Papers

Total Citations

H-Index

About

Sanghoon Kim is a robotics researcher whose work bridges perception, mobility, and reliability in autonomous systems. His primary research areas include computer vision for robotic navigation, defect detection using deep learning, and the mechanical durability of mobile platforms. Kim’s most impactful contribution is his 2021 paper on crack detection using a fully convolutional network deployed on a wall-climbing robot, which has garnered 29 citations—a strong indicator of its influence in infrastructure inspection robotics. He also advanced object detection in mobile image sensors through color segmentation (2013), and conducted foundational work on the accelerated life testing of in-wheel motors for mobile robots (2010). This latter study addressed the critical reliability of in-wheel motor systems, which eliminate traditional powertrain components to improve efficiency in electric vehicles and robots. By verifying component durability under stress, Kim’s research directly supports the safe deployment of high-performance, eco-friendly platforms. His work exemplifies a systems-level approach—combining deep learning, sensor processing, and mechanical testing—to create robots that are both intelligent and robust. For students and researchers, Kim’s portfolio offers a model of how targeted, application-driven research can yield practical tools for real-world inspection and mobility challenges.