Papers
7
Total Citations
89
H-Index
5
About
Yingpeng Dai is a leading researcher in computer vision and robotics, specializing in real-time semantic segmentation for autonomous systems. Their work focuses on developing efficient neural network architectures that enable unmanned mobile robots, including agricultural and service robots, to perceive and navigate complex environments with minimal computational overhead. Dai’s major contributions include the Efficient Dual-Branch Bottleneck Network (EDBNet) for CCD camera-based segmentation and the Multi-level Enhancement Layers Network (MELNet), which leverages Broad Learning Systems for real-time street scene understanding. Their research has garnered over 89 citations, with top papers such as “Efficient Dual-Branch Bottleneck Networks” (25 citations) and “Towards Broad Learning Networks” (20 citations) highlighting their impact. Notably, Dai has advanced practical applications like tobacco leaf maturity discrimination for harvesting robots and lane detection under challenging conditions, as well as intelligent fruit picking using marker-controlled watershed algorithms. Their work bridges the gap between deep learning efficiency and real-world robotic deployment, making autonomous systems faster, more reliable, and adaptable to dynamic environments.
Research Focus
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