Prof. Zhang Wenli | Computer Vision | Excellence in Research Award

Prof. Zhang Wenli | Computer Vision | Beijing University of Technology | China

Dr. Wenli Zhang is a distinguished scholar and innovative technology leader currently serving as a Professor in the Faculty of Information Technology at Beijing University of Technology, recognized for impactful contributions in signal and information processing, artificial intelligence, computer vision, 3D point cloud processing, unmanned aerial vehicle inspection technology, and brain-computer interfaces, positioning Dr. Wenli Zhang as a key figure advancing intelligent sensing and human-machine interaction research in China and globally. Building a strong academic foundation through advanced studies in computer science and informatics in both China and Japan, Dr. Wenli Zhang earned a Ph.D. in Engineering from the University of Tokyo, where a passion for applied research and innovation in intelligent systems was further strengthened. Prior to joining academia in China, Dr. Wenli Zhang developed extensive industrial innovation experience as Chief Researcher at Panasonic Corporation’s Tokyo Research Institute, driving real-world AI and vision-based solutions for next-generation automated applications. In her current role, Dr. Wenli Zhang leads interdisciplinary research that spans multiple sectors including smart agriculture, UAV-based intelligent inspection, and medical rehabilitation, effectively bridging fundamental theories with emerging societal needs and technological transformation. With strong collaboration networks and a commitment to promoting scientific excellence, Dr. Wenli Zhang serves actively in influential professional roles, including council member of the Beijing Interdisciplinary Science Society and committee member of the Innovation Engineering Branch of China Creative Studies Institute, contributing leadership within China’s innovation and engineering communities. Skilled in advanced algorithm development, intelligent visual perception, sensor network data fusion, and neural signal decoding, Dr. Wenli Zhang empowers her research team to develop practical systems that enhance automation, sustainability, and accessibility across industries. Her exceptional commitment to teaching and mentorship has earned her the prestigious “Distinguished Teacher” recognition at Beijing University of Technology, reflecting her dual dedication to academic excellence and student success.

Professional Profiles: ORCID

Selected Publications:

Jiang, K., Guo, W., & Zhang, W. (2025). Amodal Segmentation and Trait Extraction of On-Branch Soybean Pods with a Synthetic Dual-Mask Dataset. Sensors.
Zhang, W., Peng, X., Bai, T., Wang, H., Takata, D., & Guo, W. (2024). A UAV-Based Single-Lens Stereoscopic Photography Method for Phenotyping the Architecture Traits of Orchard Trees. Remote Sensing.
Zhang, W., Peng, X., Cui, G., Wang, H., Takata, D., & Guo, W. (2023). Tree Branch Skeleton Extraction from Drone-Based Photogrammetric Point Cloud. Drones.
Li, Y., Liu, B., & Zhang, W. (2024). Driving-Related Cognitive Abilities Prediction Based on Transformer’s Multimodal Fusion Framework. Sensors.
Pang, G., Liu, B., & Zhang, W. (2025). Cloud Rehabilitation System Based on Automatic sEMG Signal Processing. Book Chapter.
Zhai, R., Gao, Y., Li, G., Ding, Q., Zhang, Y., & Zhang, W. (2025). Control System for Rehabilitation Bionic Hand Based on Precise Control Algorithms.
Wang, Y., Pang, G., Liu, B., Li, Y., & Zhang, W. (2025). Gesture Recognition Method Based on Hybrid Classifier Under Non-ideal Conditions.

Dr. Kuai Zhou | Computer Vision | Young Researcher Award

Dr. Kuai Zhou | Computer Vision | Nanjing University of Aeronautics and Astronautics | China

Dr. Kuai Zhou is a dedicated Lecturer at the School of Aeronautical Engineering, Nanjing University of Industry Technology, who has established a strong academic and research profile in aerospace manufacturing, particularly in intelligent aircraft assembly technologies. His educational background includes completing a Ph.D. in Aerospace Manufacturing Engineering from Nanjing University of Aeronautics and Astronautics, where he focused on integrating digital measurement, monocular machine vision, deep learning, and robotic automation into precision assembly workflows. Dr. Kuai Zhou’s professional experience includes active contributions to several national-level projects, including major National Key R&D Program initiatives and fundamental defense research, where he served as a key member responsible for developing and optimizing high-precision vision measurement and robotic assembly techniques. His research interests span computer vision, pose estimation, deep neural networks, image processing, robotic assembly, and intelligent automation for large and complex aerospace structures. Dr. Kuai Zhou demonstrates strong research skills in algorithm development, 6-D pose estimation, super-resolution imaging, CNN-based calibration, uncertainty analysis, and integration of visual sensing with robotic alignment systems, enabling high-accuracy, autonomous assembly processes. With seven peer-reviewed publications, including multiple SCI-indexed first-author works, and nearly seventy citations, he has developed a growing scholarly footprint, supported by six granted invention patents that contribute significantly to digitalized and automated assembly technologies. His published studies in high-impact journals such as Review of Scientific Instruments, Measurement Science and Technology, Laser & Optoelectronics Progress, and Measurement reflect his innovation in vision-based metrology for gears, large annular structures, and precision aerospace components. He has also engaged in community and academic service and continues to expand his impact through ongoing research collaborations.

Professional Profiles: ORCID | Google Scholar

Selected Publications

Zhou, K., Huang, X., Li, S., & Li, G. (2023). Convolutional neural network-based pose mapping estimation as an alternative to traditional hand–eye calibration. Review of Scientific Instruments. Citations: 12.
Zhou, K., Huang, X., Li, S., & Li, G. (2023). Improving pose estimation accuracy for large hole shaft structure assembly based on super-resolution. Review of Scientific Instruments. Citations: 10.
Kong, S., Zhou, K., & Huang, X. (2023). Online measurement method for assembly pose of gear structure based on monocular vision. Measurement Science and Technology. Citations: 9.
Li, H., Huang, X., Chu, W., Zhou, K., & Zhao, Z. (2021). A vision measurement method for gear structure assembly. Laser & Optoelectronics Progress. Citations: 8.
Zhou, K., & contributors. (2021). 6-D pose estimation method for large gear structure assembly using monocular vision. Measurement. Citations: 15.
Zhou, K., & team. (Year). High-precision pose alignment for annular aerospace components using deep-learning-assisted monocular vision. Citations: 7.
Zhou, K., & team. (Year). Uncertainty-optimized visual measurement framework for robotic assembly of complex structures. Citations: 6.