Yongfa Mi
Papers
1
Total Citations
7
H-Index
1
About
Yongfa Mi is a researcher focused on advancing computer vision and underwater robotics, with particular expertise in multi-scale image fusion, lightweight deep learning models, and marine target detection. His most-cited work, "Research on multi-scale fusion image enhancement and improved YOLOv5s lightweight ROV underwater target detection method" (2024, 7 citations), tackles critical challenges in underwater perception: low-quality imagery, high computational costs, and poor detection accuracy. Mi proposed a novel approach that first enhances underwater images through multi-scale fusion, then optimizes the YOLOv5s architecture for deployment on resource-constrained remotely operated vehicles (ROVs). This work directly addresses the need for efficient, real-time detection in marine resource exploration and environmental monitoring. By balancing model lightness with detection precision, Mi’s contributions support practical applications in autonomous underwater systems. His research bridges image processing and deep learning, offering scalable solutions for complex underwater environments. With growing citations, Mi is establishing himself as an emerging voice in marine robotics and vision-based sensing.
Research Focus
Key Achievements
Top Papers
- 1