Min Fu

Papers

Total Citations

127

H-Index

About

Min Fu is a leading researcher in marine artificial intelligence, specializing in underwater computer vision and embedded systems for ocean monitoring. Her work addresses the critical challenge of enabling intelligent detection and classification of marine organisms in complex, low-visibility underwater environments—a key bottleneck for the marine economy and autonomous equipment. Fu’s most influential contribution is the development of an improved EfficientDet model for underwater object detection, which significantly enhances accuracy in murky waters; this 2022 paper has garnered 72 citations, reflecting its impact on the field. She also pioneered the use of MobileNet combined with transfer learning for real-time marine animal classification on embedded systems, achieving efficient, low-power identification that supports aquaculture monitoring and fishing operations. This 2019 work, with 55 citations, demonstrates her ability to bridge deep learning and practical deployment. Fu’s research is notable for its direct application to marine resource management, offering scalable solutions for growth monitoring and environmental assessment. Her achievements highlight a commitment to making AI accessible for real-world oceanographic challenges, positioning her as a key innovator in marine technology.