Yonghong Zhang

Nanjing University of Information Science and Technology, Northwestern Polytechnical University

Papers

Total Citations

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About

Yonghong Zhang is a robotics researcher whose work spans robotic manipulation, precision agriculture, and intelligent healthcare systems. His primary research focuses on robotic grasping and autonomous harvesting, where he has developed innovative deep learning architectures to address real-world challenges. Zhang’s most notable contribution is DSNet (Double Strand robotic grasp detection Network), a 2024 paper that has already garnered 18 citations. This work introduces a novel encoder-decoder structure combining a transformer branch with a U-Net branch, effectively reconciling local and global feature extraction for improved robotic grasp detection. In precision agriculture, Zhang developed an optimized YOLO-PP-based detection system for cherry tomatoes, achieving 10 citations in 2025 by addressing the complexities of cluster-based fruit detection for autonomous harvesting robots. Earlier in his career, he explored bio-inspired robotics with a study on the gait stability of a hopping kangaroo robot based on a spring-mass model (2006), and contributed to healthcare technology by designing an infusion auxiliary service system using ZigBee wireless networks (2018). Zhang’s work demonstrates a consistent commitment to bridging theoretical advances in computer vision and robotics with practical, deployable systems for industry and healthcare.