Papers
3
Total Citations
8
H-Index
2
About
Vadym Mishchuk is a researcher at the forefront of applying artificial intelligence to humanitarian demining, specializing in the intersection of computer vision, autonomous robotics, and real-time object detection. His work addresses the critical challenge of detecting explosive ordnance (EO)—including landmines and unexploded ordnance (UXO)—using deep learning models deployed on robotic systems. Mishchuk’s major contributions center on systematically evaluating the trade-offs between detection accuracy and processing speed, a vital consideration for field-deployed autonomous systems where both precision and real-time performance are essential for civilian safety. His most-cited paper (4 citations) compares YOLOv8 and RT-DETR models for EO detection, while his subsequent studies analyze performance across varying input resolutions using subsets of the COCO validation dataset. By quantifying these accuracy-speed trade-offs, Mishchuk provides a practical framework for selecting and optimizing computer vision models for mobile demining platforms. His research directly supports post-conflict recovery efforts by enabling safer, more efficient robotic clearance of hazardous areas, demonstrating a compelling application of AI to save lives and repurpose affected land.
Research Focus
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Top Papers
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