Zhanpeng Fang
Papers
1
Total Citations
46
H-Index
1
About
Zhanpeng Fang is a researcher in computer vision, with a primary focus on pedestrian detection and deep learning. His most cited work, the 2020 review “Deep learning for occluded and multi‐scale pedestrian detection: A review” (46 citations), provides a comprehensive analysis of the challenges and advancements in detecting pedestrians under difficult conditions—specifically occlusion and scale variation. This review has become a key reference for researchers working on autonomous driving, video surveillance, and robotics, synthesizing deep learning approaches that have driven the field forward. Fang’s contributions help bridge the gap between theoretical models and real-world applications, where robust detection is critical for safety and automation. His work highlights the ongoing need for algorithms that can handle complex visual environments, and his review serves as a foundational resource for students and engineers seeking to understand the state of the art in pedestrian detection.
Research Focus
Key Achievements
Top Papers
- 1Deep learning for occluded and multi‐scale pedestrian detection: A review46 citations · 2020