Junrui Xue

Papers

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About

Junrui Xue is a researcher advancing the frontiers of computer vision and autonomous robotics, with a focus on real-time perception systems for agricultural and industrial applications. His work centers on developing efficient, lightweight deep learning architectures that balance accuracy with computational feasibility—a critical requirement for deployment on resource-constrained robotic platforms. Xue’s most cited paper, "Object Detection Algorithm for Lingwu Long Jujubes Based on the Improved SSD" (2022, 16 citations), introduces a streamlined single-shot multi-box detector tailored for robotic fruit picking in natural environments, achieving enhanced precision while reducing computational complexity. This contribution directly addresses the challenge of enabling low-latency, high-accuracy detection for agricultural automation. In his subsequent work, "Multiscale Feature Extraction Network for Real-time Semantic Segmentation of Road Scenes On the Autonomous Robot" (2023, 9 citations), Xue extends his expertise to autonomous navigation, proposing a multiscale feature extraction network that delivers real-time semantic segmentation for road scenes. By prioritizing both speed and robustness, Xue’s research demonstrates a clear trajectory toward practical, deployable vision systems that empower robots to perceive and interact with dynamic, unstructured environments.