David Shea
Papers
1
Total Citations
2
H-Index
1
About
David Shea is a researcher focused on advancing autonomous underwater robotics, with particular expertise in computer vision, object detection, and machine learning for marine environments. His most notable contribution is the development of an automated collection and annotation pipeline for underwater object detection on sonar images, a critical innovation that streamlines the creation of training datasets for deep learning models. This work directly addresses a fundamental bottleneck in underwater robotics: the difficulty of obtaining labeled sonar data for tasks like exploration, inspection, and rescue. By automating the annotation process, Shea’s pipeline significantly reduces the manual effort required, accelerating the deployment of reliable detection systems on autonomous underwater vehicles. While his 2021 paper has garnered early citations, its impact lies in laying foundational infrastructure for future research in marine robotics. Shea’s work bridges the gap between practical field robotics and data-driven AI, making him a key contributor to enabling more capable and autonomous underwater systems for critical applications.
Research Focus
Key Achievements
Top Papers
- 1