Ms. Xinyue Xu | Computer Science | Research Excellence Award
Ms. Xinyue Xu | Computer Science | University of Chinese Academy of Sciences | China
Ms Xinyue Xu is a dedicated researcher affiliated with the University of Chinese Academy of Sciences, where she advances her work in intelligent defect detection, robust deep-learning architectures, and computer vision applications for industrial automation. Ms Xinyue Xu completed her doctoral studies in a computing-related discipline at the University of Chinese Academy of Sciences, focusing on lightweight neural network optimization, blur-robust models, and intelligent inspection frameworks that address real-world engineering challenges. Her professional experience includes academic research responsibilities within her institution, participation in collaborative laboratory initiatives, and involvement in international research efforts related to machine vision and high-precision defect-detection systems. Her primary research interests include computer vision, artificial intelligence, defect detection in industrial components, lightweight convolutional models, and the development of high-efficiency architectures suitable for real-time inspection tasks. Ms Xinyue Xu possesses strong research skills in model design, data-driven algorithm development, image processing, neural network compression, performance benchmarking, and experimental evaluation across diverse industrial datasets. Her ORCID profile documents her contribution to the Scopus-indexed journal article FDC-YOLO: A Blur-Resilient Lightweight Network for Engine Blade Defect Detection, where she introduces an enhanced lightweight framework capable of handling blur-induced recognition challenges. This publication demonstrates her technical rigour, methodological clarity, and ability to address practical industrial problems through advanced machine-learning models. Although early in her career, Ms Xinyue Xu exhibits promising scholarly potential and continues to strengthen her academic record. Her research visibility and contributions make her eligible for participation in professional associations such as IEEE and ACM, and her academic accomplishments position her well for future recognition in emerging researcher and innovation-focused award categories. Her honors include her contribution to high-quality peer-reviewed research published in an international journal indexed in Scopus. Moving forward, Ms Xinyue Xu is well positioned to expand her publication portfolio, engage in broader interdisciplinary collaboration, strengthen her global research presence, and contribute to the development of next-generation intelligent inspection technologies. With her commitment to scientific advancement, growing research output, and expertise in applied AI, she continues to demonstrate strong potential for leadership and long-term impact in her field.
Academic Profile: ORCID
Featured Publications:
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Xu, X., Li, F., Xiong, L., He, C., Peng, H., Zhao, Y., & Song, G. (2025). FDC-YOLO: A blur-resilient lightweight network for engine blade defect detection. Algorithms.