Mr. Chengqi Xue | Computer Vision – Student at Yangtze University, China
With a deep-rooted passion for automation, intelligent systems, and data-driven innovation, this researcher has rapidly emerged as a notable contributor to the field of machine vision and artificial intelligence. Currently pursuing a Bachelor of Engineering in Automation, the individual has already led and participated in significant research initiatives at the undergraduate level, reflecting both academic rigor and a forward-thinking approach to applied science. Focusing primarily on defect detection, industrial monitoring, and the application of machine learning to engineering materials, this individual has developed a robust foundation for impactful scientific contributions. The combination of technical proficiency, research productivity, and innovation-oriented mindset makes this nominee a strong contender for the Best Researcher Award.
Profile Verified:
🎓 Education:
The nominee is a final-year undergraduate student in the BEng Automation program at Yangtze University, China (2020–2024), with a cumulative GPA of 3.71/5.0 and a rank in the top 7% of the cohort. The academic training includes core and advanced courses such as C Language Programming, Analog and Digital Electronic Technology, Principle of Automatic Control, Computer Control Technology, Machine Vision, and Motion Control Systems. These courses have laid a solid theoretical and technical foundation, particularly in control systems, embedded programming, and computer vision, fueling the research directions taken by the candidate. The academic performance, combined with a rigorous curriculum, has prepared the nominee to handle complex research challenges and real-world applications of automation.
🧪 Experience:
The nominee’s research journey began in 2021 and includes three significant assistant roles in funded and institutionally recognized projects. In the Internal Thread Defect Detection project, the nominee engineered a computer-controlled multi-vision system using fish-eye lenses and deep learning, culminating in an intelligent defect detection system based on YOLO and GANs. Another impactful contribution includes the application of machine learning techniques in optimizing the structural performance of engineering materials such as GFRP-filled concrete and laminated glass. The candidate further developed a machine vision-based early warning system for industrial applications, integrating image acquisition, distortion correction, and predictive analytics. These roles not only enhanced the candidate’s technical and analytical capabilities but also demonstrated leadership and problem-solving skills vital for translational research in intelligent systems.
🔬 Research Interest:
The nominee’s research interests are centered on machine vision, deep learning-based detection systems, generative adversarial networks (GANs), and the intersection of automation with intelligent sensing technology. Additional focus areas include data modeling, meta-learning, genetic algorithms for data enhancement, and optimization of engineering materials using AI. This multidisciplinary focus aims to bridge the gap between traditional mechanical processes and cutting-edge digital transformation, with applications in quality control, structural safety, and predictive maintenance. The research also explores low-sample learning techniques to address data limitations in real-world industrial scenarios, reflecting an understanding of practical engineering constraints.
🏅 Award:
In recognition of exceptional contributions to automation and sensing technology, the nominee received the Best Researcher Award at the International Research Awards on Sensing Technology in September 2024. This accolade followed a rigorous evaluation of the nominee’s impactful work on machine vision-based defect detection and intelligent monitoring systems. Furthermore, the National Encouragement Scholarship was awarded in acknowledgment of academic excellence, placing the nominee in the top 4% of students at Yangtze University. These distinctions affirm the candidate’s promise as a future leader in research and development.
📚 Publication:
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🧱 Data Modeling of GFRP Tubular Filled Concrete Column using Meta Learning – PLoS ONE, 2024 📖 [Cited by: 16]
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🔍 Internal Thread Defect Detection via Multi-vision Systems – PLoS ONE, 2024 📖 [Cited by: 12]
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🧠 Design Optimization of GFRP Tubular Structures via Machine Learning – PLoS ONE, 2023 📖 [Cited by: 14]
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🤖 Defect Detection using GANs and YOLO in Industrial Threads – Sensors, 2024 📖 [Cited by: 10]
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📷 Image Calibration in Internal Thread Vision Systems – SPIE Proceedings, AASIP 2024 📖 [Cited by: 3]
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🔗 Review on Image Stitching Algorithms for Internal Thread Inspection – SPIE Proceedings, RSTIP 2024 📖 [Cited by: 4]
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🪟 Modeling of Toughened Sandwich Glass with Machine Learning – PLoS ONE, Under Review (2025)
🔚 Conclusion:
In summary, the nominee exemplifies the ideal profile for the Best Researcher Award—early but exceptional academic achievement, meaningful technical contributions, interdisciplinary research focus, and a consistent trajectory of innovation. The blend of published scholarship, applied patents, and awards reflects a promising future in advanced automation and machine vision. While still at the undergraduate level, the nominee has outpaced many peers through commitment, curiosity, and competence. Continued academic progression, such as graduate studies and international collaboration, will no doubt strengthen this solid foundation and further contribute to the research community. The nomination is therefore fully justified and well-aligned with the spirit of the award.