Daeho Kim
Papers
2
Total Citations
9
H-Index
2
About
Daeho Kim is pioneering the intersection of robotics, artificial intelligence, and construction safety, with a focus on digitizing heavy machinery and hazardous environments. His research centers on developing deep neural network (DNN) models and robotic platforms to enable semantic digital twinning of construction sites—a critical step toward sustainable, automated infrastructure management. In his highly cited 2025 work, Kim introduced the robotization of a miniature-scale radio-controlled excavator, creating a novel medium for generating construction-specific DNN training data. This addresses a persistent bottleneck: the scarcity of diverse, real-world imagery needed to train AI for digital twins of excavators. His 2024 paper further advances safety in human-robot collaboration by proposing a single-shot visual relationship detection model that accurately identifies contact-driven hazards—such as unwanted forcible contact between workers and construction robots. With citations accumulating rapidly for these foundational contributions, Kim is establishing himself as a key figure in construction robotics. His work not only pushes the boundaries of AI in civil engineering but also promises to make construction sites safer, more efficient, and more data-driven.
Research Focus
Key Achievements
Top Papers
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- 2