JongHyun Kim | Vision Sensing | Best Innovation Award

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Best Innovation Award

JongHyun Kim — Korea Institute of Industrial Technology / Department

Researcher Profile
Researcher JongHyun Kim
Affiliation Korea Institute of Industrial Technology / Department
Country South Korea
Subject Area Vision Sensing
Event Global Sensor Awards
ORCID 0000-0002-9498-4746

JongHyun Kim is affiliated with the Korea Institute of Industrial Technology and is associated with research in the field of Vision Sensing. The profile presented here recognizes the researcher in connection with the Best Innovation Award under the Global Sensor Awards. Vision sensing encompasses technologies that enable machines and intelligent systems to acquire, interpret, and respond to visual information through cameras, optical components, image-processing methods, and computational sensing techniques. Such technologies form an important part of modern sensing systems and can support applications in industrial inspection, automation, robotics, intelligent manufacturing, mobility, and monitoring.

Abstract

This academic recognition profile presents JongHyun Kim of the Korea Institute of Industrial Technology in the context of the Best Innovation Award associated with Vision Sensing. Vision sensing integrates optical acquisition, image formation, signal processing, computer vision, and intelligent interpretation to transform visual observations into actionable information. Research and development in this area contributes to the advancement of automated inspection, intelligent manufacturing, robotics, quality assessment, and other sensor-enabled systems. The award profile acknowledges the relevance of innovation-oriented research within this multidisciplinary sensing domain while maintaining a distinction between verified researcher information and information that has not been supplied.

Keywords

  • Best Innovation Award
  • Vision Sensing
  • Computer Vision
  • Optical Sensing
  • Intelligent Manufacturing
  • Industrial Sensors
  • Machine Vision
  • Sensor Technology

Introduction

Vision sensing is a major branch of modern sensing technology in which visual and optical information is captured and converted into measurable data. Contemporary vision systems may combine imaging devices, illumination, optics, embedded processing, algorithms, and machine-learning techniques to identify objects, evaluate surfaces, measure dimensions, recognize patterns, and monitor processes. Classical developments in feature detection and object recognition established important methodological foundations for computer vision and machine perception. [1] Research on rapid object detection subsequently demonstrated the potential for visual analysis to operate efficiently in practical environments. [2]

Within industrial environments, vision sensing can support non-contact measurement and automated decision-making. Its integration with manufacturing systems can enable inspection processes that are repeatable, scalable, and capable of operating alongside automated equipment. The field therefore connects sensor engineering with image analysis, artificial intelligence, robotics, and industrial automation.

Research Profile

JongHyun Kim is identified with the Korea Institute of Industrial Technology / Department and the subject area of Vision Sensing. The available profile information associates the researcher with the development and application context of sensing technologies relevant to visual information acquisition and interpretation. The research profile is considered within the broader interdisciplinary environment of industrial technology, where sensing systems can contribute to manufacturing intelligence, process monitoring, inspection, and automation.

The research area of Vision Sensing requires interaction among optical engineering, sensor hardware, image processing, computational methods, and application-specific system design. Innovation in this field can involve improvements in sensing accuracy, acquisition speed, robustness, environmental adaptability, data interpretation, or integration with other industrial systems.

Research Contributions

The available information does not provide a complete publication list or detailed project record for JongHyun Kim. Accordingly, specific technical achievements are not attributed beyond the supplied affiliation and research subject area. The following contribution domains describe the academic and technological context in which Vision Sensing research is commonly evaluated.

  • Visual Information Acquisition: Development or application of sensing approaches for acquiring reliable visual information from physical environments.
  • Machine Vision: Application of imaging and computational techniques to support automated identification, inspection, measurement, or classification.
  • Industrial Sensing: Integration of vision-based sensing with industrial processes and intelligent manufacturing environments.
  • Sensor-Data Interpretation: Processing and analysis of visual information to generate meaningful measurements or decisions.
  • Innovation in Sensing: Exploration of approaches that improve the practical usefulness, reliability, scalability, or integration of vision-based sensing technologies.

Publications

A verified publication list, Scopus author identifier, document count, citation count, and h-index were not included in the supplied profile information. For academic accuracy, no individual publication has therefore been attributed to JongHyun Kim in this article without supporting bibliographic information. The DOI references listed below provide scholarly background for the broader field of computer vision and visual sensing rather than representing publications attributed to the researcher.

Relevant foundational literature demonstrates how computational approaches to visual feature extraction and object detection have influenced the development of modern machine-vision systems. [1] [2]

Research Impact

Vision sensing has broad technological relevance because visual information can be collected without physical contact and subsequently processed for measurement, inspection, classification, and control. In manufacturing, these capabilities can contribute to automated quality assessment, defect detection, dimensional evaluation, production monitoring, and process optimization. The combination of sensing hardware and computational intelligence also creates opportunities for increasingly autonomous industrial systems.

The potential impact of research in this area is therefore measured not only through scholarly outputs but also through improvements in practical sensing performance, system integration, reliability, and applicability. A complete quantitative assessment of JongHyun Kim’s individual research impact would require verified bibliometric and publication data, which were not supplied for this profile.

Award Suitability

The Best Innovation Award is conceptually aligned with research and development that introduces meaningful advances in technology, methodology, system design, or practical application. JongHyun Kim’s identified affiliation with the Korea Institute of Industrial Technology and subject area of Vision Sensing place the profile within a technological domain where innovation can have direct relevance to intelligent sensing and industrial applications.

Based on the information supplied, the strongest areas of relevance include the relationship between Vision Sensing and industrial technology, the interdisciplinary nature of modern visual sensing, and the potential application of vision-based systems to automated environments. A formal evaluation of specific innovations would require supporting evidence such as publications, patents, project outcomes, technical demonstrations, documented deployments, or other verifiable research outputs.

Strengths for the Award: The profile is positioned within Vision Sensing, an established and rapidly developing area of sensor technology with applications across industrial automation and intelligent manufacturing. The association with a technology-focused research institution further provides an appropriate institutional context for innovation-oriented research.

Areas for Further Documentation: Detailed publication records, patents, technical achievements, citation metrics, project outcomes, and documented implementation results would strengthen the evidence base for evaluating the researcher’s individual contribution and innovation impact.

Conclusion

JongHyun Kim is presented in this academic recognition profile as a researcher affiliated with the Korea Institute of Industrial Technology / Department and associated with the field of Vision Sensing. The Best Innovation Award provides a framework for recognizing research and technological development that contributes to advancement in sensing applications. Vision sensing remains an important area connecting optical acquisition, computational analysis, machine vision, and intelligent industrial systems. Further bibliographic and technical documentation would enable a more comprehensive assessment of the researcher’s individual scholarly and technological contributions.

References

  1. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
    https://doi.org/10.1023/B:VISI.0000029664.99615.94
  2. Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154.
    https://doi.org/10.1109/TPAMI.2004.1263516
  3. ORCID. (n.d.). ORCID record: JongHyun Kim, ORCID iD 0000-0002-9498-4746.
    https://orcid.org/0000-0002-9498-4746
  4. Global Sensor Awards. (n.d.). Global Sensor Awards.
    https://globalsensorawards.com/

Mr. MingShou An | Computer Vision | Research Excellence Award

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Mr. MingShou An | Computer Vision | Research Excellence Award

Mr. MingShou An | Computer Vision | Xi’an Technological University | China

Mr. MingShou An is an accomplished researcher and academic professional specializing in Artificial Intelligence–driven sensing systems, with strong expertise in Computer Vision, Deep Learning, and Intelligent Monitoring Technologies. He holds a Ph.D. in an engineering and computing discipline from a recognized Chinese university, where his doctoral research focused on data-driven visual perception, lightweight neural architectures, and intelligent sensing models for real-time environments. Currently affiliated with Xi’an Technological University, Mr. MingShou An has established a solid academic and professional profile through his contributions to national and collaborative research projects addressing smart surveillance, intelligent safety monitoring, and applied AI for sensor-enabled systems. His professional experience spans academic lecturing, research supervision, and applied system development, where he actively bridges theoretical algorithm design with deployable AI solutions for real-world sensing applications, including edge AI and time-sensitive visual analytics. His scholarly output includes 10 Scopus-indexed publications, achieving 15 citations and an h-index of 3, with several works published in IEEE-affiliated conferences and peer-reviewed venues related to intelligent perception and lightweight network design.

Citation Metrics (Scopus)

20

15

10

5

0

Citations
15

Documents
10

h-index
3

🟦 Citations
🟥 Documents
🟩 h-index

View Scopus Profile
 View ORCID Profile

Featured Publication

Mr. Suresha R | Computer Vision Awards | Excellence in Research Award

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Mr. Suresha R | Computer Vision Awards | Excellence in Research Award 

Mr. Suresha R | Computer Vision Awards | Amrita Vishwa Vidyapeetham | India

Mr. Suresha R. is a results-driven educator and technologist with over six years of combined experience in teaching computer science and academic leadership. He holds an M.Sc. in Computer Science and has qualified in UGC-NET and K-SET, while currently pursuing a Ph.D. Mr. Suresha R. has demonstrated expertise in curriculum design and research, particularly focusing on AI in autonomous solutions and computer vision applications. In his professional career, Mr. Suresha R. has served as an Assistant Professor at Amrita Vishwa Vidyapeetham, School of Computing, Mysuru Campus, and at SBRR Mahajana First Grade College, Mysuru, where he delivered advanced courses in Computer Vision, Digital Image Processing, Pattern Recognition, Computational Intelligence, Computer Graphics, Machine Learning, Exploratory Data Analysis, R Programming, Information Retrieval, Data Mining, Numerical Analysis, and Operations Research, consistently achieving high student satisfaction. His research interests encompass small traffic sign detection and recognition in challenging scenarios using computer vision and LiDAR-based techniques with ROS2 framework, deep learning-based vehicle detection and distance estimation for autonomous systems, motion blur image restoration, wild animal recognition through vocal analysis, and SVM-based medical image classification. Mr. Suresha . possesses strong research skills in Python, MATLAB, ROS2, machine learning, deep learning, image processing, and data analysis. He has successfully guided Bachelor’s and Master’s students in research projects, fostering innovation and academic growth. His academic contributions are recognized through multiple publications in prestigious journals and conferences, including IEEE Access, Procedia Computer Science, ICCCNT, CCEM, ICECAA, and INDIACom. Mr. Suresha . has a proven record of collaborating in interdisciplinary teams, effectively communicating complex technical concepts, and mentoring students to achieve excellence in research and practical applications. His dedication to lifelong learning and active engagement in both teaching and research demonstrates his commitment to advancing knowledge in computer science and autonomous systems. Throughout his career, Suresha  has received awards and recognitions for research excellence, contributing to the development of sustainable and intelligent solutions in the field of computer vision and AI. Overall, Mr. Suresha exemplifies a passionate and innovative professional, bridging theoretical foundations with applied research, and continues to make significant contributions to academia and technology

Professional Profiles: ORCID

Selected Publications 

  1. Suresha, R., Manohar, N., Ajay Kumar, G., & Singh, R. (2024). Recent advancement in small traffic sign detection: Approaches and dataset.

  2. Suresha, R., Manohar, N., & Jipeng, T. (2024). Two-stage traffic sign classification system.

  3. Sudharshan Duth, P., Manohar, N., Suresha, R., Priyanka, M., & Jipeng, T. (2024). Wild animal recognition: A vocal analysis.

  4. Suresha, R., Jayanth, R., & Shriharikoushik, M. A. (2023). Computer vision approach for motion blur image restoration system.

  5. Srinivasa, C., Suresha, R., Manohar, N., Dharun, G. K., Sheela, T., & Jipeng, T. (2023). Deep learning-based techniques for precise vehicle detection and distance estimation in autonomous systems.

  6. Suresha, R., Devika, K. M., & Prabhu, A. (2022). Support vector machine classifier based lung cancer recognition: A fusion approach.

Dr. Kuai Zhou | Computer Vision | Young Researcher Award

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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

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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.