Papers
1
Total Citations
2
H-Index
1
About
Dr. Qiong Zhang is a researcher whose work sits at the intersection of computer vision and efficient deep learning, with a particular focus on enabling real-time target recognition for resource-constrained platforms like drones, vehicle-mounted systems, and robots. Her most-cited paper, "Target recognition algorithm based on improved depth separable convolution" (2020), tackles a critical challenge in mobile AI: achieving high-accuracy object detection within the tight computational and memory budgets of embedded devices. By innovating on depthwise separable convolution architectures, Dr. Zhang’s work directly addresses the trade-off between model performance and operational efficiency, making deep learning algorithms more deployable in real-world, low-latency environments. While her citation count is still growing, the practical relevance of her research—bridging the gap between theoretical advances in convolutional neural networks and the constraints of edge computing—positions her as a promising voice in the field. Her contributions are particularly valuable for students and engineers seeking to understand how to adapt state-of-the-art recognition models for mobile and autonomous systems, where every millisecond and megabyte counts.
Research Focus
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Top Papers
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