Doss Amala
Papers
2
Total Citations
17
H-Index
2
About
Doss Amala’s research bridges the critical gap between artificial intelligence and materials science, focusing on underwater autonomous systems and corrosion protection. Her most cited work, a two-part case study, demonstrates this unique interdisciplinary approach. In Part A, she leverages deep learning and computer vision to detect metal objects in underwater environments, addressing a key challenge for autonomous deep-sea exploration and resource utilization. Part B extends this work by investigating the corrosion protection of mild steel—the material often used in underwater robots—using paint coatings to resist the aggressive chloride ions in seawater. With her top papers accumulating 10 and 7 citations respectively, Amala’s contributions are significant for advancing the safety and longevity of underwater operations. By combining intelligent object detection with practical corrosion mitigation strategies, her research directly supports the development of more robust and autonomous deep-sea technologies, offering valuable insights for engineers and researchers working on marine robotics and infrastructure protection.
Research Focus
Key Achievements
Top Papers
- 1
- 2