Toru Iwao
Papers
2
Total Citations
6
H-Index
2
About
Toru Iwao is a researcher advancing the field of infrastructure inspection through edge-based computer vision. His work focuses on the automated detection of components in power transmission line (PTL) systems, aiming to replace costly and dangerous manual inspections using helicopters or climbing robots. Iwao’s key contributions involve deploying state-of-the-art object detection models—specifically YOLOv3 and YOLOv7—on low-cost, single-board computers like the Raspberry Pi. His 2023 paper on YOLOv3 for PTL component detection has garnered 4 citations, while his 2024 follow-up using the more efficient YOLOv7 architecture has earned 2 citations. By demonstrating that high-accuracy detection of critical hardware is feasible on resource-constrained platforms, Iwao’s work paves the way for scalable, real-time drone-based inspection systems. This research directly addresses the practical challenge of maintaining high-voltage transmission networks, offering a cost-effective solution that could significantly improve grid reliability and worker safety. Iwao’s focus on deploying modern AI on accessible hardware marks him as a contributor to the growing field of tinyML for industrial applications.
Research Focus
Key Achievements
Top Papers
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- 2