Jinxing Niu

About

Jinxing Niu is a leading researcher in agricultural robotics and intelligent perception systems, with a focus on deep learning for object recognition, localization, and scene understanding. His work spans underwater waste management, indoor robotics, and orchard automation, where he develops real-time, lightweight algorithms for complex environments. Niu’s major contributions include improved YOLO-based models for underwater waste recognition (8 citations), apple pose estimation via SCH-YOLO11s segmentation (6 citations), and occlusion avoidance for harvesting robots. His research on indoor object recognition and dense reconstruction (7 combined citations) has advanced service robot and augmented reality applications. Notably, his orchard robot navigation using an improved RTAB-Map algorithm and apple trajectory prediction with YOLOv8-EK-IPF demonstrate his impact on precision agriculture. With over 25 total citations across his most-cited works, Niu’s innovative integration of computer vision and robotics addresses critical challenges in environmental cleanup and automated harvesting, making him a key figure in applied AI for real-world systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Underwater Waste Recognition and Localization Based on Improved YOLOv5

   8 citations · 2023
2. 2
   Apple Pose Estimation Based on SCH-YOLO11s Segmentation

   6 citations · 2025
3. 3
   Real-Time Recognition and Location of Indoor Objects

   4 citations · 2021
4. 4
   Real-Time Dense Reconstruction of Indoor Scene

   3 citations · 2021
5. 5
   Orchard Robot Navigation via an Improved RTAB-Map Algorithm

   2 citations · 2025
6. 6
   Occlusion Avoidance for Harvesting Robots: A Lightweight Active Perception Model

   2 citations · 2026
7. 7
   Apple Trajectory Prediction in Orchards: A YOLOv8-EK-IPF Approach

   1 citations · 2025
8. 8
   Deep learning techniques for point cloud tasks: a review

   1 citations · 2025

Key Collaborators

Contact & Links