Jianhan Nie
Papers
2
Total Citations
15
H-Index
2
About
Jianhan Nie is a researcher specializing in computer vision and deep learning, with a particular focus on agricultural automation and environmental monitoring. His work centers on developing advanced object detection algorithms that address real-world challenges in complex, unstructured environments. Nie's most significant contribution is the YOLO-CT method, published in 2025, which enhances the YOLOv8n-Pose architecture to detect multi-species mature cherry tomatoes and precisely locate picking points in intricate agricultural settings—a breakthrough for robotic harvesting that has already garnered 9 citations. Additionally, his YOLOv7-GCM algorithm, introduced in 2024, tackles the critical issue of creek waste detection by improving the YOLOv7 model, achieving 6 citations for its practical application in environmental cleanup. By integrating pose estimation and attention mechanisms into the YOLO family, Nie demonstrates a talent for tailoring state-of-the-art models to specific, high-impact problems. His work not only advances the field of computer vision but also provides tangible solutions for agriculture and sustainability, making him a promising voice in applied AI research.
Research Focus
Key Achievements
Top Papers
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- 2