Lifeng Zhang

Papers

Total Citations

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About

Lifeng Zhang is a researcher at the forefront of robotic perception and autonomous systems, bridging the gap between extreme environment exploration and precision agriculture. His work centers on developing real-time visualization and 3D object detection technologies that enable machines to perceive and interact with complex, unstructured environments. Zhang’s early contribution, the “Real‐Time Visualization System for Deep‐Sea Surveying” (2014, 18 citations), advanced remote robotic exploration by improving the detection of mines and objects in the inaccessible deep sea, demonstrating his commitment to solving challenges in hazardous, human-limited settings. More recently, his pioneering study on “Corn pose estimation using 3D object detection and stereo images” (2025, 8 citations) introduces the Stereo-Corn-Pose Detection method, a novel approach that automates key agricultural tasks—such as precise pesticide spraying and robotic picking—by accurately estimating corn dimensions and orientation. This work directly supports the growing field of smart farming, where robotic arms require fine-grained spatial understanding. Though his citation counts reflect a focused, emerging impact, Zhang’s research trajectory shows a clear pattern: he engineers robust, real-world perception systems that empower robots to operate where humans cannot or should not, from the ocean floor to the cornfield.