Quanyang Liu

Changchun University of Science and Technology

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About

Quanyang Liu is a researcher focused on advancing deep learning for real-world, resource-constrained applications, particularly in the domains of drones, vehicle-mounted systems, and robotics. Their key research areas include computer vision, target recognition, and efficient neural network architectures. Liu’s major contribution lies in developing improved depth separable convolution techniques, which significantly reduce computational complexity and model size while maintaining high accuracy—a critical need for deploying AI on mobile and embedded devices. Their most-cited work, “Target recognition algorithm based on improved depth separable convolution” (2020), has garnered 2 citations and addresses the pressing challenge of enabling real-time, accurate object detection on platforms with limited memory and processing power. This work exemplifies Liu’s commitment to bridging the gap between cutting-edge deep learning algorithms and practical, deployable systems. Through their research, Liu is helping to make intelligent perception more accessible and efficient for autonomous and mobile technologies, paving the way for smarter drones, vehicles, and robots that can operate effectively in the field.