Papers
1
Total Citations
12
H-Index
1
About
Qi Tao is a researcher at the forefront of intelligent infrastructure maintenance, specializing in computer vision and deep learning for railway engineering. His work focuses on developing efficient, real-time detection systems for critical rail components, particularly fastener screws—a vital yet often overlooked element of track safety. In his landmark 2024 paper, Tao introduced FSS-YOLO, a lightweight model built on YOLOv5n that enables rapid, image-based screw detection for maintenance robots. This innovation addresses a pressing need for automation in rail inspection, balancing accuracy with computational efficiency for on-board deployment. With 12 citations already, his work is gaining traction among researchers and engineers seeking practical AI solutions for transportation infrastructure. Tao’s contributions exemplify the growing intersection of robotics and deep learning, offering scalable tools to enhance safety and reduce manual labor in railway maintenance. His research promises to accelerate the adoption of intelligent inspection systems in real-world industrial settings.
Research Focus
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Top Papers
- 1