Owing to the strong emphasis on interconnectivity and automation in this fourth industrial revolution, unmanned aerial vehicle (UAV) technologies have become increasingly important in applications involving real-time monitoring, remote sensing, and asset inspection activities. In their application to asset inspection, particularly bridge inspection, UAVs have the capability of ensuring: (1) safety for both the inspection team and the public, (2) reduction in the total inspection time, (3) reduction in total cost of inspection, and (4) the ease of access of the hard-to-reach areas on the structures.
In this talk, an end-to-end operational workflow for streamlining the integration of UAVs into bridge inspection will be discussed. This workflow provides definitive guidelines and tools for the estimation and quantification of the procedures involved in UAV-assisted bridge inspections. In addition, the use of ensemble learning will be discussed for automated concrete bridge defect detection and classification in order to analyze the data collected by UAVs during bridge inspections.
Mr. Emmanuel Attah Marfo is a Graphics Hardware Engineer at Intel Corporation. He received his Master's from North Carolina A&T State University in Electrical and Computer Engineering in 2023 and a bachelor’s degree in Electrical and Electronics Engineering from the University of Energy and Natural Resources in Ghana in 2017. As a researcher at the Autonomous Cooperative Control of Emergent Systems of Systems (ACCESS) laboratory, Mr. Marfo researched the integration of UAVs into bridge inspections and the automation of the defect detection and classification processes into aerial image data collection and analysis.