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About

Huilin Ge is a versatile researcher whose work spans computer vision, robotics, and intelligent systems, with particular depth in facial expression recognition, spray painting robot trajectory optimization, and underwater object detection. His 2022 paper on deep learning-based facial expression recognition has amassed an impressive 172 citations, establishing him as a meaningful contributor to affective computing and human-computer interaction. Earlier foundational work in robotic trajectory planning—developing Bézier-Bernstein algorithms and point cloud slicing techniques for spray painting robots—demonstrates a rigorous engineering foundation, with his 2020 trajectory planning paper earning 56 citations and influencing automation in industrial surface coating applications. In recent years, Ge has pivoted toward underwater robotics and environmental intelligence. His 2024 YOLOv8-based underwater trash detection model, already garnering 74 citations, reflects both timely ecological relevance and strong technical innovation, addressing the urgent challenge of aquatic pollution monitoring through advanced real-time detection architectures. Subsequent contributions including YOLO-Underwater-Tiny and a newly released underwater detection dataset underscore his commitment to building robust, lightweight solutions for resource-constrained underwater systems. Across his career, Ge has demonstrated a compelling ability to bridge fundamental machine learning research with high-impact real-world robotics applications.