Danxu Wang

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About

Danxu Wang is a researcher advancing the field of computer vision, with a primary focus on underwater target detection and small object recognition. His most cited work, "YOLOX-DC: A Small Target Detection Network up to Underwater Scenes" (2022), addresses a critical challenge in marine robotics: enabling underwater operation robots to achieve both high detection accuracy and rapid processing speed. By enhancing the YOLOX architecture, Wang developed a one-stage detection algorithm specifically optimized for the complex, low-visibility conditions of underwater environments. This contribution has garnered 7 citations, reflecting its relevance to autonomous underwater vehicles and marine exploration systems. Wang's research bridges the gap between deep learning theory and practical robotic applications, offering solutions that improve the reliability of object detection in challenging aquatic scenes. His work is particularly valuable for students and engineers seeking efficient, real-time detection networks tailored to non-ideal imaging conditions, positioning him as a notable contributor to the intersection of computer vision and underwater robotics.