Papers
1
Total Citations
2
H-Index
1
About
Dr. Jinhe Ran is a leading researcher in computer vision and multimodal perception, with a primary focus on advancing pedestrian detection technologies for safety-critical applications like autonomous driving and intelligent surveillance. His most impactful work, "GLNet-YOLO: Multimodal Feature Fusion for Pedestrian Detection," introduces a novel architecture that fuses visible and thermal infrared imagery to overcome the limitations of single-modal detection in challenging conditions such as low light or occlusion. This contribution, already garnering 2 citations shortly after its 2025 publication, addresses a fundamental gap in robust, real-world object detection. Dr. Ran’s research is distinguished by its practical engineering approach, bridging deep learning innovation with deployable solutions for dynamic environments. His work not only pushes the boundaries of multimodal fusion but also sets a new benchmark for accuracy and reliability in pedestrian detection, making him a rising voice in the field. For students and researchers, Dr. Ran’s contributions offer a compelling model of how targeted algorithmic design can solve persistent challenges in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1GLNet-YOLO: Multimodal Feature Fusion for Pedestrian Detection2 citations · 2025