Andrii Podorozhniak
Papers
1
Total Citations
2
H-Index
1
About
Andrii Podorozhniak is a researcher focused on the intersection of computer vision and safety-critical applications, with a particular emphasis on real-time object detection for humanitarian demining. His work addresses the crucial trade-off between detection accuracy and processing speed, a challenge central to deploying AI in field robotics. In his 2024 study on explosive ordnance detection, Podorozhniak systematically evaluated state-of-the-art object detectors on subsets of the COCO validation dataset, providing a practical framework for selecting models that balance precision with the rapid inference required for landmine and UXO identification. This contribution is vital for developing autonomous systems that can operate effectively in hazardous environments. With his research already garnering citations, Podorozhniak is establishing himself as a key voice in applied deep learning for security and defense, bridging the gap between algorithmic performance and real-world deployment constraints.
Research Focus
Key Achievements
Top Papers
- 1