Dr. Kuai Zhou | Computer Vision | Young Researcher Award

Posted on by Global Sensor Awards

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 

  1. 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.

  2. 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.

  3. 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.

  4. Li, H., Huang, X., Chu, W., Zhou, K., & Zhao, Z. (2021). A vision measurement method for gear structure assembly. Laser & Optoelectronics Progress. Citations: 8.

  5. Zhou, K., & contributors. (2021). 6-D pose estimation method for large gear structure assembly using monocular vision. Measurement. Citations: 15.

  6. Zhou, K., & team. (Year). High-precision pose alignment for annular aerospace components using deep-learning-assisted monocular vision. Citations: 7.

  7. Zhou, K., & team. (Year). Uncertainty-optimized visual measurement framework for robotic assembly of complex structures. Citations: 6.

Mr. Ahmet Serhat Yildiz | Computer Vision Awards | Best Researcher Award

Posted on by Global Sensor Awards

Mr. Ahmet Serhat Yildiz | Computer Vision Awards | Best Researcher Award

Mr. Ahmet Serhat Yildiz | Computer Vision Awards | Brunel University of London | United Kingdom

Mr. Ahmet Serhat Yildiz is an emerging researcher in sensing technology with growing expertise in machine learning, deep learning, embedded systems, and multi-sensor fusion, demonstrating strong potential for advanced research roles and academic leadership. He is currently pursuing his PhD in Electronic and Computer Engineering at Brunel University London, where he focuses on real-time object detection, semantic 3D depth sensing, LiDAR–camera fusion, and intelligent autonomous perception systems, aligning closely with sensing applications in robotics, transportation, surveillance, and industrial automation. His academic foundation includes degrees in electronics, electrical engineering, business management, and extensive English language training, providing a multidisciplinary perspective that strengthens his analytical and communication abilities. His professional experience includes roles as a Graduate Teaching Assistant in digital design, embedded systems, and computer architecture, as well as serving as an IoT facilitator, where he mentored learners and contributed to community-oriented technology initiatives. Mr. AHMET SERHAT YILDIZ has developed notable research projects, including FPGA-based embedded game systems, PLC-controlled industrial automation setups, and biomedical sensing circuits for pulse wave velocity measurement, demonstrating strong hands-on engineering skills. His research portfolio includes Scopus-indexed publications on YOLO-based detection models, sensor fusion for autonomous vehicles, and real-time navigation using LiDAR and deep learning frameworks, reflecting his ability to integrate theory with practical sensing applications. His technical skills include Python, PyTorch, embedded C, FPGA development, digital circuit design, PLC programming, and multi-sensor signal processing, enabling him to contribute to both algorithmic and hardware-oriented research environments. His achievements include scholarly publications, increasing citation impact, and recognition through participation in international conferences and multidisciplinary research projects.

Professional Profiles: ORCID | Google Scholar

Featured Publications 

  1. Alkandary, K., Yildiz, A. S., & Meng, H. (2025). A comparative study of YOLO series (v3–v10) with DeepSORT and StrongSORT: A real-time tracking performance study. Electronics.

  2. Tunali, M. M., Yildiz, A., & Çakar, T. (2022). Steel surface defect classification via deep learning. International Conference on Computer Science and Engineering (UBMK).

  3. Yildiz, A. S., Meng, H., & Swash, M. R. (2025). Real-time object detection and distance measurement enhanced with semantic 3D depth sensing using camera–LiDAR fusion. Applied Sciences.

  4. Tunali, M. M., Sayar, A., Aslan, Y., Mutlu, İ., & Çakar, T., including Yildiz, A. (2023). Enhancing quality control in plastic injection production: Deep learning-based detection and classification of defects. International Conference on Computer Science and Engineering (UBMK).

  5. Yıldız, A., Mişe, P., Çakar, T., Terzibaşıoğlu, A. M., & Öke, D. (2023). Spine posture detection for office workers with hybrid machine learning. International Conference on Computer Science and Engineering (UBMK).

  6. Yildiz, A. S., Meng, H., & Swash, M. R. (2025). YOLOv8–LiDAR fusion: Increasing range resolution based on image-guided sparse depth fusion in self-driving vehicles. Lecture Notes in Networks and Systems.

  7. Yildiz, A. S., Meng, H., & Swash, M. R. (2024). A multi-sensor fusion approach to real-time bird’s-eye view navigation: YOLOv8 and LiDAR integration for autonomous systems. Korkut Ata Scientific Research Conference Proceedings.