Mr. Xiaotian Wang | Engineering Award | Excellence in Research
Associate Professor at Northwestern Polytechnical University, China
Xiaotian Wang, an Associate Professor at Northwestern Polytechnical University, holds both a Master’s and a Ph.D. degree, earned in 2016 and 2020, respectively, from the same institution. His academic background and current position within a renowned university are strong indicators of his expertise and commitment to research. His specialized focus on computer vision and remote sensing image processing, particularly in object detection and tracking, aligns with cutting-edge technological advancements in these fields. Wang’s contributions to unmanned systems research further highlight his alignment with contemporary research trends and his potential to lead innovative projects.
Professional Profile
Education
Dr. Xiaotian Wang completed his Master’s degree and Ph.D. in the field of Computer Vision and Remote Sensing from Northwestern Polytechnical University, Xi’an, China. He earned his Master’s in 2016 and his Ph.D. in 2020, both from the same prestigious institution. Throughout his education, Dr. Wang developed a deep understanding of machine learning, artificial intelligence, and their applications in unmanned systems. His academic journey involved rigorous research in object detection and tracking algorithms, which he continued to develop through various academic and practical projects. His research contributions were shaped by the rigorous training and mentorship he received during his graduate studies. Dr. Wang’s education provided him with a strong theoretical foundation and the technical expertise necessary to conduct pioneering research in remote sensing image processing and computer vision, making him a recognized expert in his field.
Experience
Dr. Xiaotian Wang is currently an Associate Professor at Northwestern Polytechnical University, where he has made significant contributions to research and development in unmanned systems and remote sensing. His primary focus is on computer vision, particularly object detection and tracking technologies that have important applications in surveillance, robotics, and unmanned vehicles. Prior to his current position, Dr. Wang has been actively involved in several key research projects, collaborating with national and international researchers in the development of cutting-edge technologies for unmanned systems. His expertise in integrating computer vision algorithms with remote sensing has led to several innovative solutions in the field. Additionally, he actively mentors graduate students and early-career researchers, guiding them in advancing their knowledge and research skills in these high-tech domains. His academic and research experience provides a foundation for developing practical, scalable solutions in remote sensing and unmanned technologies.
Research Focus
Conclusion
Publications Top Noted
Cross-Attention-Driven Adaptive Graph Relational Network for Multilabel Remote Sensing Scene Classification”
Authors: Bi, H., Chang, H., Wang, X., Hong, D.
Citations: 0
Year: 2024
Journal: IEEE Transactions on Geoscience and Remote Sensing
Volume: 62
Article ID: 5224414
“Complexity Evaluation of Aerial Infrared Countermeasure Scenes”
Authors: Xie, F., Dong, M., Wang, X., Yang, D., Yan, J.
Citations: 0
Year: 2024
Journal: IEEE Transactions on Aerospace and Electronic Systems
“Can Rumor Detection Enhance Fact Verification? Unraveling Cross-Task Synergies Between Rumor Detection and Fact Verification”
Authors: Jin, W., Jiang, M., Tao, T., Zhao, B., Yang, G.
Citations: 0
Year: 2024
Journal: IEEE Transactions on Big Data
“A Research on Rapid Assessment of Cross-Domain Perceptual Fidelity for Practical Applications”
Authors: Tao, W., Wang, X., Yan, T., Zeng, Q., Lu, R.
Citations: 0
Year: 2024
Conference: Proceedings of the 3rd Conference on Fully Actuated System Theory and Applications (FASTA 2024)
“An Improved Small Infrared Target Detection Algorithm Based on Yolov5”
Authors: Wang, X., Yang, Z., Sun, Y., Qian, C., Zhao, Y.
Citations: 0
Year: 2024
Conference: Lecture Notes in Electrical Engineering (LNEE), 1175, pp. 405–413
“Detection of Occlusion-Resistant Based on Improved YOLOv7”
Authors: Tao, W., Wang, K., Li, Y., Yan, T., Wang, X.
Citations: 0
Year: 2024
Conference: Lecture Notes in Electrical Engineering (LNEE), 1173, pp. 430–439
“ESF-YOLO: an accurate and universal object detector based on neural networks”
Authors: Tao, W., Wang, X., Yan, T., Liu, Z., Wan, S.
Citations: 0
Year: 2024
Journal: Frontiers in Neuroscience
Volume: 18
Article ID: 1371418
“An Infrared Small Target Detection Method Based on Attention Mechanism”
Authors: Wang, X., Lu, R., Bi, H., Li, Y.
Citations: 3
Year: 2023
Journal: Sensors (Basel, Switzerland)
Volume: 23
Issue: 20
“SiamCAR-Kal: anti-occlusion tracking algorithm for infrared ground targets based on SiamCAR and Kalman filter”
Authors: Fu, G., Zhang, K., Yang, X., Tian, X., Wang, X.T.
Citations: 0
Year: 2023
Journal: Machine Vision and Applications
Volume: 34
Issue: 3
Article ID: 43
“Robust small infrared target detection using multi-scale contrast fuzzy discriminant segmentation”
Authors: Wang, X., Xie, F., Liu, W., Tang, S., Yan, J.
Citations: 5
Year: 2023
Journal: Expert Systems with Applications
Volume: 212
Article ID: 118813