Qingqi Wu

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About

Qingqi Wu is a leading researcher in computer vision and deep learning, with a primary focus on real-time object detection in challenging environments. His most-cited work, "Real-time underwater target detection based on improved YOLOv7" (2025), has already garnered 10 citations, reflecting its immediate impact on autonomous underwater systems and marine robotics. This paper introduces novel architectural enhancements to the YOLOv7 framework, addressing critical issues like low visibility, color distortion, and dynamic lighting in underwater scenes. Wu's contributions are particularly significant for advancing autonomous underwater vehicles (AUVs) and environmental monitoring, where reliable detection is essential. His research bridges the gap between state-of-the-art deep learning and practical deployment in resource-constrained, real-time settings. By optimizing detection speed without sacrificing accuracy, Wu's work has set a new benchmark for underwater vision tasks. His achievements demonstrate a rare ability to translate complex algorithmic improvements into tangible, field-ready solutions, making him a rising authority in applied computer vision.