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About

Fenglin Wei’s research lies at the intersection of underwater robotics, computer vision, and human–robot interaction, with a particular focus on enabling seamless communication between divers and autonomous underwater vehicles (AUVs). Their most cited work, “Diver’s hand gesture recognition and segmentation for human–robot interaction on AUV” (2021, 20 citations), establishes foundational methods for interpreting diver gestures in challenging underwater conditions. This is complemented by “An Underwater Human–Robot Interaction Using a Visual–Textual Model for Autonomous Underwater Vehicles” (2022, 16 citations), which addresses the unique visual challenges of the marine environment—such as light attenuation and turbidity—through a multimodal approach. Wei further advances the field with “A Method for Underwater Human–Robot Interaction Based on Gestures Tracking with Fuzzy Control” (2021, 11 citations), integrating gesture tracking with adaptive control systems. Their most recent contribution, “A Lightweight Underwater Instance Segmentation Method Based on YOLOv8 and RFAHead” (2024, 4 citations), tackles real-time object identification on resource-constrained AUVs, demonstrating a commitment to practical, deployable solutions. With a cumulative impact of over 50 citations, Wei’s work is shaping the future of intuitive, robust human–robot collaboration in one of Earth’s most demanding environments.