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About

Nikolaos Bakalos is a researcher at the forefront of intelligent infrastructure monitoring, specializing in the integration of unmanned aerial vehicles (UAVs) and deep learning for civil engineering applications. His primary research areas encompass computer vision, automated defect detection, and robotics-enabled road maintenance. Bakalos’s major contribution lies in pioneering the use of UAV-based visual data and advanced AI architectures—such as the YOLOv8 framework—to automate the localization and inspection of critical road elements, including defects and removable urban pavements (RUPs). This work directly addresses the limitations of traditional, time-consuming, and costly manual road surveys. His most-cited paper, "Real time road defect monitoring from UAV visual data sources" (2023, 11 citations), establishes a foundational methodology for real-time, aerial infrastructure assessment, significantly improving road safety and maintenance efficiency. Further notable achievements include his contributions to robotics-enabled roadwork upgrading, as detailed in a 2024 book chapter, and the development of automated systems for identifying modular pavement components. Through his research, Bakalos is driving a paradigm shift toward more responsive, cost-effective, and data-driven infrastructure management.