Yung-Yao Chen

National Taipei University of Technology, National Taiwan University of Science and Technology

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About

Yung-Yao Chen’s research bridges computer vision, robotics, and autonomous systems, with a focus on enabling machines to perceive and interact with their environments intelligently. His major contributions span thermal-based pedestrian detection for nighttime surveillance, robot vision for object and pose recognition in pick-and-place operations, and scene categorization using advanced sparse coding techniques. Notably, his 2019 work on thermal pedestrian detection using Faster R-CNN with a region decomposition branch has garnered 20 citations, demonstrating its impact on intelligent surveillance. His 2015 robot vision system for recognizing both objects and their rotations, cited 11 times, directly advances robot programming by demonstration. Chen also introduced a learning-based heatmap-guided model for monocular visual odometry (2025), addressing challenges in autonomous navigation under dynamic lighting conditions. Through these works, Chen has contributed practical solutions to real-world problems in automation, surveillance, and autonomous vehicles, with his research cited collectively over 40 times. His work exemplifies the integration of deep learning and traditional vision techniques to create robust, deployable systems.