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About

Qunhui Yang is a researcher advancing the frontiers of autonomous systems and underwater perception. Her primary research areas span deep learning-based underwater object detection, multi-robot collaborative localization, and computer vision for challenging environments. Yang’s most impactful contribution is her work on real-time underwater object detection, where she developed deep learning models capable of operating in complex, low-visibility underwater conditions—a critical technology for marine environmental monitoring, resource exploration, and ecological protection. Her paper on this topic has garnered 72 citations, underscoring its significance to the field. Additionally, she has made notable strides in multi-robot systems, proposing innovative laser rangefinder-based methods for collaborative localization that overcome the limitations of low GPS accuracy in outdoor settings. Her work on an improved YOLOv5 algorithm specifically addresses the challenge of blurry underwater images, enhancing detection precision for objects of varying sizes. Yang’s research is particularly valuable for applications in marine fisheries monitoring and logistics, where reliable perception and positioning are essential. Her contributions represent important steps toward more robust, real-world autonomous systems in demanding environments.