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A platform for research: civil engineering, architecture and urbanism
Automated method for airfield pavement condition index evaluations
Highlights A drone was used to fly over an airfield and collect image data for inspection purposes. A convolutional neural network was trained for semantic segmentation on airfield pavement data to detect cracks and utility patches. PCI values could be calculated from drone image data using a series of computer algorithms.
Abstract Infrastructure inspection and maintenance is a necessary, and often costly, process required for civil engineering structures, which has led to a movement toward automated technologies within this domain. This paper describes a method for conducting a partially automated airfield pavement condition assessment, similar to the manual ASTM Pavement Condition Index (PCI), using drone mounted imaging technology. Aerial images were processed using a convolutional neural network trained for pavement distress detection to generate a condition index. Automated data collected on a single auxiliary airfield in Colorado Springs, CO produced a condition index value of 56.5, which strongly agreed with manual inspection results that calculated a PCI value of 54 for the same runway. These results indicate that automation of some time-intensive aspects of pavement evaluation is feasible using drone-captured images and machine learning. The results of this study represent a single test case; future work should focus on expanding this framework to include the effects of location-dependent variability.
Automated method for airfield pavement condition index evaluations
Highlights A drone was used to fly over an airfield and collect image data for inspection purposes. A convolutional neural network was trained for semantic segmentation on airfield pavement data to detect cracks and utility patches. PCI values could be calculated from drone image data using a series of computer algorithms.
Abstract Infrastructure inspection and maintenance is a necessary, and often costly, process required for civil engineering structures, which has led to a movement toward automated technologies within this domain. This paper describes a method for conducting a partially automated airfield pavement condition assessment, similar to the manual ASTM Pavement Condition Index (PCI), using drone mounted imaging technology. Aerial images were processed using a convolutional neural network trained for pavement distress detection to generate a condition index. Automated data collected on a single auxiliary airfield in Colorado Springs, CO produced a condition index value of 56.5, which strongly agreed with manual inspection results that calculated a PCI value of 54 for the same runway. These results indicate that automation of some time-intensive aspects of pavement evaluation is feasible using drone-captured images and machine learning. The results of this study represent a single test case; future work should focus on expanding this framework to include the effects of location-dependent variability.
Automated method for airfield pavement condition index evaluations
Pietersen, RA (author) / Beauregard, MS (author) / Einstein, HH (author)
2022-06-02
Article (Journal)
Electronic Resource
English
Drones , Pavement , Airfield pavement , Infrastructure inspection , Automation , Deep learning , Convolutional neural networks , AGL , Above ground level , APCI , Airfield pavement condition index , APE , Airfield pavement evaluation , ASPP , Atrous spatial pyramid pooling , CAD , Computer-aided design , CNN , Convolutional neural network , FAA , Federal aviation administration , FOD , Foreign object debris , HTOL , Horizontal take-off landing , MAV , Micro air vehicle , NAV , Nano air vehicle , NDAA , National defense authorization act , PAV , Pico air vehicle , PCI , Pavement condition index , SD , Smart dust , UAV , Unmanned air vehicle , Micro unmanned air vehicle , USAFA , United states air force academy , VTOL , Vertical take-off landing
Airfield Pavement Condition Assessment
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