Mingyan Wu
Papers
1
Total Citations
29
H-Index
1
About
Dr. Mingyan Wu is a leading researcher in computer vision and underwater robotics, specializing in small target detection under challenging environmental conditions. Their most notable contribution is the development of the Underwater Small Target Detection (USTD) network, a two-stage architecture that leverages deformable convolutional pyramids to address severe object deformation, occlusion, and scenario diversity in underwater imagery. This work, published in 2022 and already garnering 29 citations, directly tackles the limitations of general object detectors in aquatic environments, offering a robust solution for marine surveillance and autonomous underwater vehicle navigation. By integrating adaptive feature learning with spatial transformations, Wu’s approach significantly improves detection accuracy for small, distorted targets—a critical advancement for ocean exploration and defense applications. Their research bridges the gap between theoretical deep learning and practical underwater sensing, with potential impacts on environmental monitoring and offshore infrastructure inspection. Dr. Wu’s innovative methodology continues to inspire further studies in domain-specific object detection, marking them as a rising authority in vision-based underwater systems.
Research Focus
Key Achievements
Top Papers
- 1Underwater Small Target Detection Based on Deformable Convolutional Pyramid29 citations · 2022