Papers
3
Total Citations
45
H-Index
3
About
Mykhailo Protsenko is a researcher advancing the frontiers of computer vision and artificial intelligence for unmanned aerial systems (UAS). His work centers on enhancing object detection and recognition in aerial imagery, specifically addressing the critical challenges of automating surveillance and monitoring tasks performed by drones and robots. Protsenko’s major contributions include the development of an advanced recognition method that integrates convolutional neural networks with discrete wavelet transforms, significantly improving the accuracy of identifying monitored objects. He has also refined object detection models for aerial photographs and video, leveraging deep convolutional neural networks to boost performance in real-world UAS applications. With his most-cited papers accumulating over 45 citations, his research is gaining traction among peers working on autonomous systems and remote sensing. Notably, his 2021 and 2022 studies provide practical improvements to neural network architectures, directly impacting how unmanned systems perceive and interpret their environment. Protsenko’s work is essential reading for students and engineers seeking to understand the intersection of deep learning and aerial robotics, offering tangible solutions for more reliable and intelligent drone-based monitoring.
Research Focus
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