Xiangjing An

National University of Defense Technology

Papers

Total Citations

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About

Xiangjing An is a leading researcher in the fields of computer vision, robotics, and autonomous navigation, with a particular focus on vision-based perception for field robots. His major contributions include developing efficient deep learning architectures for object detection in robotic applications, notably his 2017 work on "Efficient deep network for vision-based object detection in robotic applications" (33 citations), which addresses the critical challenge of real-time performance in resource-constrained robotic systems. An's pioneering research on "Vision Sensor-Based Road Detection for Field Robot Navigation" (2015, 24 citations) introduced a hierarchical method for robust road detection in challenging outdoor environments, significantly advancing the capability of field robots to navigate autonomously in unstructured terrains. His work has been instrumental in bridging the gap between deep learning and practical robotic perception, enabling more reliable environmental understanding for applications ranging from agricultural robotics to autonomous vehicles. Through his innovative approaches to sensor-based perception, An has established himself as a key contributor to the development of intelligent robotic systems that can operate effectively in complex, real-world environments.