Weidong Fang

Shanghai Institute of Microsystem and Information Technology

Papers

Total Citations

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About

Weidong Fang is a leading researcher at the intersection of computer vision and intelligent communication systems. His primary research areas encompass deep learning-based video object tracking and the optimization of 5G communication networks for industrial IoT applications, particularly within the challenging environment of intelligent mines. Fang’s most notable contribution is his comprehensive review on video object tracking using deep learning, which has garnered 35 citations and serves as a foundational reference for researchers tackling complex computer vision challenges like video surveillance and human-computer interaction. In parallel, he has pioneered work on periodic monitoring and filtering suppression techniques to mitigate signal interference in mine 5G communications, a critical advancement for enabling unmanned driving, intelligent robotics, and real-time industrial control in underground settings. His research directly addresses the high-bandwidth, low-latency demands of modern IoT systems, bridging the gap between theoretical deep learning models and practical communication reliability. Through these contributions, Fang has established himself as a key figure in advancing both autonomous visual tracking and robust wireless infrastructure for next-generation smart environments.