Papers
1
Total Citations
2
H-Index
1
About
Fuling Li is a rising researcher in computer vision and multimodal perception, whose work focuses on advancing object detection in challenging real-world environments. Li’s most notable contribution is the development of a novel RGB-D fusion framework that integrates a dual-encoder feature aggregation mechanism with the YOLOv11 architecture, specifically designed to overcome the limitations of unimodal visual detection under low illumination, occlusion, and texture-sparse conditions. This symmetric dual-branch approach, processing both RGB images and depth maps, has already garnered early attention with 2 citations since its 2025 publication, signaling its potential impact on autonomous systems and robotics. Li’s research addresses a critical gap in robust visual perception, offering a practical solution for scenarios where traditional single-sensor methods fail. By bridging deep learning with multimodal sensor fusion, Li is contributing to the next generation of reliable, real-time detection systems. As a young scholar, Li’s work exemplifies the growing importance of combining complementary data modalities to achieve state-of-the-art performance in complex, unstructured environments.
Research Focus
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Top Papers
- 1