Yikui Zhai

Wuyi University, China University of Petroleum, Beijing

Papers

Total Citations

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About

Yikui Zhai is a researcher whose work bridges computer vision and intelligent robotics, with a particular focus on facial expression recognition and autonomous navigation. His key contributions lie in developing weakly supervised learning frameworks for emotion analysis, exemplified by his 2019 paper on facial expression recognition using a transferred Deep Active Learning Convolutional Neural Network (DAL-CNN) with active incremental learning, which has garnered 18 citations. This work addresses the challenge of limited labeled data in affective computing, enabling more efficient and scalable emotion detection systems. More recently, Zhai has advanced the field of mobile robotics with his 2025 paper on the GAA-DFQ model—a dual-layer learning framework for robot path planning in dynamic environments. This innovative approach integrates genetic algorithms for global path planning with the Dynamic Window Approach (DWA), fuzzy control, and Q-learning for local obstacle avoidance, achieving robust navigation in complex, changing settings. The model's dual-optimization strategy represents a significant step toward more adaptive and intelligent autonomous systems. Zhai's research demonstrates a versatile ability to tackle both perceptual and navigational challenges, making him a notable contributor to the intersection of machine learning and robotics.