Yin Jia

Singapore University of Technology and Design, China Southern Power Grid (China)

Papers

Total Citations

H-Index

About

Yin Jia is a robotics researcher focused on enhancing the safety and autonomy of service robots through deep learning and multi-sensor fusion. Their key research areas include context-aware navigation, hazardous object detection, and intelligent coverage planning for mobile robots operating in dynamic environments. Jia’s most cited work, "Deep-Learning-Based Context-Aware Multi-Level Information Fusion Systems for Indoor Mobile Robots Safe Navigation" (2023, 10 citations), addresses the critical challenge of detecting low-feature hazards like escalators, stairs, and glass doors—objects that conventional detectors often miss. This contribution directly improves functional safety for autonomous cleaning robots. In another notable study (2023, 4 citations), Jia integrates microphone-based sound event detection with deep learning to enhance context awareness and coverage planning, reducing accident risks in public spaces. Earlier work includes a power transmission line repair robot (2022, 1 citation), demonstrating versatility in applying visual control methods to infrastructure maintenance. With a growing citation record, Yin Jia’s research is paving the way for safer, more reliable service robots in real-world settings, making their work essential reading for students and engineers in autonomous systems and human-robot interaction.