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About

Qingwu Li is a researcher at the forefront of underwater robotics and computer vision, whose work bridges the gap between autonomous systems and real-world environmental monitoring. His primary research areas include stereo vision, 3D reconstruction, and deep learning-based object detection, with a strong focus on underwater and aerial platforms. Li’s most impactful contribution is his work on an ROV-based binocular vision system for underwater structure crack detection and width measurement, which has garnered 53 citations and demonstrates a practical solution for infrastructure inspection in challenging subsea environments. He has also advanced UAV technology through stereo vision SLAM-based 3D reconstruction, enabling drift-free state estimation for large-scale aerial mapping. In stereo matching, Li developed a method using census cost over cross window with segmentation-based disparity refinement, improving accuracy for applications in 3D reconstruction and robot navigation. Most recently, he has applied deep learning to marine biology, proposing an improved YOLOv8 method for detecting shallow sea creatures in cluttered, motion-blurred underwater scenes. Li’s work consistently addresses real-world challenges, from infrastructure maintenance to ecological monitoring, making him a notable figure in applied computer vision and robotics.