Dr. Faisal Saeed | Object Detection | Excellence in Research Award

Published on by Robotics and Automation

Dr. Faisal Saeed | Object Detection | Excellence in Research Award

Shenzhen University | China

Dr. Faisal Saeed is an ai research scientist specializing in computer vision, deep learning, and intelligent manufacturing, with a strong research portfolio built through advanced academic training and international research appointments. He earned his master’s combined Ph.d. in computer science from Kyungpook National University, South Korea, where his work focused on transformer-based architectures for industrial small-object detection, culminating in the thesis feature enhanced assignment-based detection transformer for industrial small object detection. His academic contributions include 21 documents, a growing research footprint of 738 citations, and an h-index of 10, reflecting the global impact of his work across ai-driven automation, defect detection, and predictive maintenance. Professionally, he has served as a university research assistant and later as a postdoctoral fellow in both South Korea and China, contributing to deep learning theory, medical image analysis, multimodal ai, Object Detection and industrial visual inspection systems. His research integrates digital twins, time-series forecasting, and transformer models to advance intelligent manufacturing and robotics. Committed to bridging theoretical innovation with real-world applications, Dr. Saeed continues to publish influential work, secure funding for emerging ai research, and contribute to the scientific community through teaching, collaboration, and cutting-edge industrial ai development.

Profile: Scopus | Google Scholar

Featured Publications

Shah, H. A., Saeed, F., Yun, S., Park, J. H., Paul, A., & Kang, J. M. (2022). A robust approach for brain tumor detection in magnetic resonance images using finetuned EfficientNet. IEEE Access, 10, 65426–65438.

Saeed, F., Paul, A., Rehman, A., Hong, W. H., & Seo, H. (2018). IoT-based intelligent modeling of smart home environment for fire prevention and safety. Journal of Sensor and Actuator Networks, 7(1), 11.

Saeed, F., Paul, A., Karthigaikumar, P., & Nayyar, A. (2020). Convolutional neural network based early fire detection. Multimedia Tools and Applications, 79(13), 9083–9099.

Saeed, F., Ahmed, M. J., Gul, M. J., Hong, K. J., Paul, A., & Kavitha, M. S. (2021). A robust approach for industrial small-object detection using an improved faster regional convolutional neural network. Scientific Reports, 11(1), 23390.

Rehman, A., Rathore, M. M., Paul, A., Saeed, F., & Ahmad, R. W. (2018). Vehicular traffic optimisation and even distribution using ant colony in smart city environment. IET Intelligent Transport Systems, 12(7), 594–601.

Mr. Xinwei He – Object Detection – Best Researcher Award

Published on by Robotics and Automation

Mr. Xinwei He - Object Detection - Best Researcher Award

Foshan University - China

Author Profile

GOOGLE SCHOLAR

Summary


Xinwei He, a second-year master’s student in software engineering at foshan university, is an emerging researcher in the field of object detection with a strong inclination toward real-world applications in power electronics and automation systems. his key contribution—tgcpn: two-level grid context propagation network for 3d small object detection—showcases a blend of academic depth and practical insight. through active participation in publication, code sharing, and peer-reviewed processes, xinwei is steadily building a credible academic presence. while early in his career, his focused research direction and commitment to quality reflect significant potential for future impact.

Early academic pursuits


Xinwei he is currently a second-year master's student in software engineering at foshan university. with a foundational understanding of computational models and a growing proficiency in research methodology, xinwei began contributing to the academic domain early in postgraduate studies. the integration of power Object Detection electronics principles with intelligent object recognition marked a turning point in xinwei's academic direction. xinwei is steadily building a credible academic presence. while early in his career, his focused research direction and commitment to quality reflect significant potential for future impact.

Professional endeavors


Though still in the academic phase, xinwei he has demonstrated professional maturity through the successful development of the project titled tgcpn: two-level grid context propagation network for 3d small object detection. this work highlights the Object Detection application of computer vision within high-efficiency environments, including power electronics-based automation systems. the project underscores a capacity to work at the intersection of deep learning and real-world utility.

Contributions and research focus


Xinwei’s primary area of research is object detection, with an emphasis on small-scale 3d object identification in complex environments. the recently completed paper revision and response to peer review comments demonstrates not only technical proficiency but also academic resilience. this effort contributes meaningfully to current advancements Object Detection in image detection technologies within power electronics and robotics-based systems.

Impact and influence


Xinwei he's ongoing work is gaining attention through platforms like github and springer. by leveraging code sharing and academic publication, Xinwei is establishing a transparent and collaborative research footprint. Object Detection although citation indices are in early stages, the methodological rigor and thematic relevance suggest promising growth in academic influence, particularly in the automation sector.

Academic cites


The project has been recognized in academic channels, with a publication under a reputable journal indexed by springer. while specific citation metrics are pending, xinwei's engagement with high-impact areas such as Object Detection power electronics-driven automation systems, signal propagation, and detection architecture indicates future relevance in citations and scholarly reference.

Legacy and future contributions


Xinwei he is poised to contribute further to the fields of object detection and robotics. future plans likely include expanding research collaborations, deepening the interface between object detection and applied power electronics, and increasing journal presence. with a strong ethical foundation as expressed in the self-declaration, xinwei seeks to build a legacy of integrity, innovation, and interdisciplinary growth.

Notable Publications

Title: Tgcpn: Two-level grid context propagation network for 3D small object detection
Authors: Y. Zhou, L. Pu, X. Xu, C. Yi, X. He, Y. Zhou, Y. Xu
Journal: Pattern Analysis and Applications

Conclusion


Xinwei He exemplifies the qualities of a dedicated and innovative researcher whose early contributions are already aligning with critical technological trends like automation and power electronics-driven systems. his consistent efforts in refining research output, responding to scholarly critique, and collaborating through open platforms position him well for long-term influence. with continued mentorship and engagement, xinwei is set to evolve into a valuable contributor to the global robotics and software engineering community.