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About

Yinyi Lai is an emerging researcher whose work sits at the intersection of computer vision, autonomous robotics, and intelligent systems for challenging real-world environments. Their research spans two compelling domains: precision agriculture and underwater robotics, reflecting a broader mission to deploy intelligent perception and navigation solutions where conventional methods fall short. In agricultural technology, Lai has developed EdgeFormer-YOLO, a lightweight multi-attention detection framework designed to identify red fruits in complex orchard environments with high accuracy and edge-deployment efficiency — a critical advancement for autonomous harvesting robots. In the underwater domain, Lai has contributed meaningfully to both perception and navigation, authoring a comprehensive review of deep learning-based underwater image enhancement and proposing Deep-Sea A*+, an advanced path planning algorithm that integrates an enhanced A* search with dynamic window approaches to help autonomous underwater vehicles navigate extreme deep-sea conditions. Although Lai's publication record is recent, with citations accumulating across works published in 2024 and 2025, the breadth of contribution across detection, image restoration, and autonomous navigation signals a researcher with versatile technical expertise. Students interested in robotics, marine exploration, or smart agriculture will find Lai's growing body of work both practically motivated and technically rigorous.