Ms. Ruoyu Yang | Machine Vision | Young Researcher Award

University of Electronic Science and Technology of China | China

Yang Ruoyu is an emerging researcher specializing in computer vision, image enhancement, and deep learning algorithms, particularly for nighttime road scenarios. Currently pursuing a master’s degree in Big Data Technology and Engineering at the University of Electronic Science and Technology of China, Yang holds a bachelor’s degree in Information and Computing Science from Harbin University of Science and Technology. His research is driven by a passion for improving image quality in challenging conditions, leading to impactful publications in journals like Displays and Applied Sciences. He has contributed to algorithmic advancements for electronic rearview mirrors, addressing low-light, glare, and illumination issues through novel deep learning architectures such as DELIA-Net and NRGS-Net. Yang’s work spans academic papers, conference presentations, patents, and practical engineering projects. Recognized with multiple academic awards, including the National Encouragement Scholarship, he is steadily building a profile as a promising innovator in intelligent transportation imaging technologies

Profile

ORCID

Education

Yang Ruoyu’s academic journey began at Harbin University of Science and Technology, where he pursued a bachelor’s degree in Information and Computing Science. This period provided him with a strong grounding in mathematics, data analysis, and computational algorithms. His early academic success was marked by multiple academic prizes and a national scholarship, highlighting his commitment to excellence. Building on this foundation, Yang advanced to the University of Electronic Science and Technology of China to undertake a master’s degree in Big Data Technology and Engineering. Here, he has deepened his expertise in data-driven problem-solving, computer vision, and artificial intelligence applications. His master’s research focuses on enhancing image processing algorithms to tackle low-light and glare issues in automotive imaging systems. Combining theoretical understanding with hands-on project experience, Yang has cultivated a skill set that bridges mathematical rigor with cutting-edge AI methodologies, positioning him for significant contributions in the field of intelligent transportation technologies.

Experience

Yang Ruoyu has gained valuable research and project experience in image processing, with a primary emphasis on nighttime driving scenarios. His most notable project involves developing a high-dynamic-range imaging algorithm for electronic rearview mirror systems, designed to perform reliably under strong lighting contrasts, low-light environments, and glare interference. Leveraging deep learning architectures such as GANs and Uformer, Yang created innovative frameworks for nighttime road image enhancement and glare suppression. Beyond theoretical design, he has tested and refined these models for real-world automotive applications. As first author, Yang has published in peer-reviewed journals like Displays and Applied Sciences, and has contributed to international conferences with impactful presentations. His work demonstrates a rare blend of academic precision and practical applicability, making him a valuable contributor to AI-powered vision systems. In addition, his patented inventions highlight his ability to move from concept to implementation, driving innovation in both academic and industrial contexts

Awards and Honors

Yang Ruoyu’s academic excellence has been recognized through a series of prestigious awards and honors. He was the recipient of the National Encouragement Scholarship, a testament to his outstanding academic performance and research potential. His consistent achievement earned him multiple Academic Third Prizes from Harbin University of Science and Technology, reflecting his high standing among peers. Beyond academic accolades, Yang’s skill in language proficiency is marked by his commendable CET-4 and CET-6 scores, which strengthen his capacity for international collaboration and research communication. His inventive spirit has been officially recognized through granted Chinese invention patents in nighttime image enhancement and glare suppression technologies. These honors collectively underscore his dedication, intellectual capability, and innovative mindset. Each award not only validates his past accomplishments but also positions him as a promising researcher with the drive and expertise to tackle complex challenges in AI-based image processing and intelligent automotive systems

Research Focus

Yang Ruoyu’s research centers on developing advanced imaging algorithms for nighttime driving assistance systems, with a focus on electronic rearview mirrors. His work tackles the twin challenges of low-light visibility and glare interference, which are critical safety concerns in real-world driving conditions. Drawing on deep learning models such as generative adversarial networks (GANs) and lightweight Uformer architectures, Yang has proposed novel solutions like DELIA-Net for detail enhancement and local illumination adjustment, as well as NRGS-Net for glare suppression. These methods improve image clarity, contrast, and reliability under extreme lighting variations. His research bridges theoretical advancements in AI with direct automotive applications, aiming to enhance driver safety and situational awareness. By integrating machine learning, computer vision, and practical engineering, Yang’s work represents a significant contribution to the future of intelligent transportation, ensuring that image-based systems perform optimally in challenging environmental conditions both in research and industrial deployment

Publications

Title: NRGS-Net: A Lightweight Uformer with Gated Positional and Local Context Attention for Nighttime Road Glare Suppression

Year:2025

Conclusion

Yang Ruoyu is a dedicated and innovative researcher whose expertise in AI-driven image processing, particularly for nighttime road enhancement and glare suppression, combines strong academic foundations, practical project execution, patented technologies, and internationally recognized publications to deliver impactful solutions for intelligent automotive systems.