Sakshi Singh

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About

Sakshi Singh is an emerging researcher specializing in underwater computer vision, marine robotics, and autonomous systems. Her work focuses on advancing the capabilities of underwater autonomous vehicles (AUVs) through robust object detection and synthetic data generation — challenges that are critical for marine exploration and environmental monitoring applications. Singh's most notable contribution is the development of the Common Objects Underwater (COU) dataset, a pioneering resource containing approximately 10,000 instance-segmented images of man-made objects across diverse aquatic and marine environments, collected during real-world underwater robot field trials. This dataset directly addresses the scarcity of high-quality annotated underwater imagery that has long hindered progress in the field. Complementing this, her IBURD (Image Blending for Underwater Robotic Detection) pipeline offers an innovative solution for generating realistic synthetic training data, enabling deep learning detectors to perform reliably in challenging underwater conditions for marine debris detection tasks. Though early in her research career, Singh's work has already garnered meaningful attention — accumulating 7 citations across just two 2025 publications — signaling strong community interest. Her contributions are particularly timely given growing global concerns around ocean health and the expanding role of autonomous systems in marine conservation efforts.