Papers
2
Total Citations
27
H-Index
2
About
Xuanzhi Liu is a researcher at the forefront of agricultural automation and intelligent robotic systems, with a primary focus on deep learning-based visual perception for complex environments. His most impactful work, the 2023 paper "Improved YOLOv7-Tiny Complex Environment Citrus Detection Based on Lightweighting" (22 citations), introduces the YOLO-DCA model—a lightweight detection framework that overcomes challenges like light variation, branch occlusion, and fruit overlap in citrus orchards. By replacing standard convolutions with depth-separable convolutions (DWConv), Liu significantly reduces model complexity while maintaining high accuracy, enabling real-time, deployable solutions for precision agriculture. In related work, he developed a "Food Package Recognition and Sorting System Based on Structured Light and Deep Learning" (5 citations), which integrates 3D structured light with vision algorithms to guide robotic arms for flexible, automated grasping and sorting. Liu’s contributions bridge the gap between theoretical computer vision and practical field robotics, offering scalable tools for smart farming and industrial automation. His research is particularly notable for its emphasis on lightweight architectures, making advanced AI accessible for resource-constrained, real-world applications.
Research Focus
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Top Papers
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