Papers
2
Total Citations
13
H-Index
1
About
Jingjing Lou is a researcher at the forefront of intelligent robotics and machine vision, with a focus on enabling precise, adaptive manipulation in complex environments. Her work bridges computer vision and robotic control, particularly in the domains of crawling robot tracking and object grasping. Lou’s most cited paper, “Crawling robot manipulator tracking based on Gaussian mixture model of machine vision” (2021, 12 citations), introduces a probabilistic approach to enhance robot tracking accuracy in dynamic settings. More recently, her 2025 study, “A Deep Learning-Based Method for Object Workpiece Recognition and Grasp Detection,” tackles the critical challenge of reliable target detection under uneven lighting conditions. In this work, she proposes YOLO-Net, a deep learning network integrating feature fusion and attention mechanisms to improve both recognition and grasp detection. This contribution is vital for industrial automation, where robust perception is key. With a growing citation record and a clear trajectory toward real-world robotic applications, Lou is establishing herself as an emerging voice in intelligent manipulation systems.
Research Focus
Key Achievements
Top Papers
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