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About

Nico Surantha is a researcher at the forefront of applying edge computing and deep learning to critical industrial infrastructure. His primary research areas span industrial wireless networks, real-time object detection, and power system inspection automation. Surantha’s major contributions lie in developing lightweight, high-performance computer vision models—specifically YOLOv3 and YOLOv7—optimized for resource-constrained single-board computers like the Raspberry Pi, enabling real-time detection of power transmission line components. This work directly addresses the challenge of automating routine inspections for high-voltage electric power systems, moving beyond traditional methods like line crawling and helicopter surveys. His most-cited paper (2023, 4 citations) demonstrates this approach, while a subsequent study (2025, 3 citations) extends the work to edge computing platforms for overhead power lines. Earlier, Surantha also made notable contributions to industrial automation with a secure, fast WLAN system featuring zero-delay roaming for controlling industrial robots in large-scale factory environments (2016, 3 citations). By bridging the gap between advanced AI and practical, deployable hardware, Surantha’s research is paving the way for safer, more efficient, and autonomous infrastructure monitoring.