Ningwei Qin
Papers
3
Total Citations
85
H-Index
3
About
Ningwei Qin is a researcher advancing the frontier of underwater computer vision, with a focus on image enhancement and object detection. Their work tackles the fundamental challenges of underwater imaging—color distortion, contrast reduction, and light attenuation—which are critical for autonomous underwater robotics and marine exploration. Qin’s most impactful contribution is the FW-GAN, a generative adversarial network with multi-scale fusion for underwater image enhancement, which has garnered 67 citations since 2022. This work established a new benchmark for restoring visual clarity in degraded underwater scenes. Building on this, Qin proposed MCRNet, a multi-color space residual network that further refines color correction and contrast restoration, achieving 11 citations. For practical deployment, Qin developed U-ATSS, a lightweight and accurate one-stage underwater object detection network (7 citations), addressing the need for efficient, real-time performance on resource-constrained underwater platforms. Through these innovations, Qin has demonstrated a consistent ability to bridge the gap between high-quality image restoration and computationally efficient detection, making their work highly relevant for both academic research and industrial applications in marine robotics and environmental monitoring.
Research Focus
Key Achievements
Top Papers
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