Serhii Shevchenko

National University of Civil Protection of Ukraine

Papers

Total Citations

H-Index

About

Serhii Shevchenko is a researcher advancing the field of computer vision and autonomous systems, with a primary focus on object detection and recognition in aerial imagery. His major contributions lie in enhancing the accuracy and efficiency of deep learning models, specifically convolutional neural networks (CNNs), for analyzing images captured by unmanned aerial vehicles (UAVs). Shevchenko’s most cited work, "Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform" (2021, 17 citations), introduces a novel integration of wavelet transforms to preprocess image data, significantly improving the detection of monitored objects. In his follow-up study, "Improvement of the model of object recognition in aero photographs using deep convolutional neural networks" (2021, 12 citations), he refines recognition models to better handle the unique challenges of aerial photography, such as varying scales and perspectives. Together, these papers demonstrate Shevchenko’s impact on automating UAV-based surveillance and monitoring, with his methods directly supporting tasks like infrastructure inspection and environmental observation. His work is a valuable resource for students and researchers exploring the intersection of deep learning and remote sensing.