Will Douglas

National University of Ireland, Maynooth

Papers

Total Citations

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About

Will Douglas is a researcher advancing the frontiers of efficient computer vision for resource-constrained platforms. His primary focus lies in developing lightweight deep learning models for real-time object detection, particularly optimized for deployment on robots and edge devices. His most cited work, "Faster YOLO-LITE: Faster Object Detection on Robot and Edge Devices" (2022), introduces a streamlined variant of the YOLO architecture that achieves significant speed improvements without sacrificing accuracy, enabling practical AI applications in autonomous systems and IoT hardware. This contribution addresses a critical bottleneck in edge computing, where computational power and energy are limited. With 10 citations, this paper has already influenced subsequent research in model compression and real-time inference. Douglas’s work is notable for bridging the gap between state-of-the-art detection algorithms and real-world deployment constraints, making him a key figure in the push toward accessible, efficient AI for robotics and embedded systems. His research continues to inspire students and engineers seeking to implement high-performance vision on low-power devices.