Lingling Li
Papers
1
Total Citations
12
H-Index
1
About
Lingling Li is a researcher in computer vision and deep learning, with a focus on pedestrian detection in large-scale, high-resolution imagery. Her most cited work, "Region NMS-based deep network for gigapixel level pedestrian detection with two-step cropping" (2021, 12 citations), introduces an innovative approach to detecting pedestrians in gigapixel-level images—a challenging domain where traditional methods struggle with computational and accuracy constraints. Li's key contribution lies in developing a two-step cropping strategy combined with a region-based non-maximum suppression (NMS) technique, which efficiently processes massive images while maintaining detection precision. This work addresses a critical gap in real-world applications like surveillance and autonomous driving, where high-resolution feeds are common. Though her citation count reflects an emerging career, the paper's practical significance and technical novelty highlight her potential to influence scalable computer vision systems. Li's research underscores a commitment to bridging algorithm efficiency and real-world deployment, making her a promising voice in the field of large-scale visual recognition.
Research Focus
Key Achievements
Top Papers
- 1