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About

Xuan-Thuy Vo is a computer vision researcher whose work focuses on making intelligent visual recognition systems practical for real-world deployment, particularly in resource-constrained environments. His research spans license plate detection in unusual conditions, gender recognition from facial images, agricultural automation, and human-robot interaction. Vo’s major contributions include developing a triple detector based on Feature Pyramid Networks for robust license plate recognition under challenging conditions (8 citations), and proposing a gender recognizer combining CNNs with Bottleneck Transformer Encoders for CPU-based real-time performance (4 citations). He has also advanced agricultural robotics with an efficient vision-based tomato classification system (2 citations), and improved human-robot interaction through facial attribute recognition using efficient multi-perspective convolution in his EMPCNet architecture (2 citations). Additionally, his work on robust hand detection integrates convolutional neural networks with attention modules for gesture recognition (2 citations). Vo’s research consistently addresses the critical challenge of deploying accurate computer vision models on low-cost devices, making his work particularly valuable for practical applications in traffic monitoring, robotics, and automated agriculture.