Research on FOD Detection for Airport Runway based on YOLOv3
Peng Li, Huajian Li
Abstract
Foreign object debris (FOD) on airport runways is a threatening factor to aircraft taking off and landing. Accurately detection of foreign objects debris is important to ensure aircraft flight safety. In this paper, a detection algorithm based on YOLOv3(You Only Look Once) for foreign objects debris is presented. This method employs deep residual network to extract feature and multi-scale feature fusion to detect small-scale FOD. Sample datasets of foreign object debris are established to validate our proposed method. The experiments show that the detection algorithm based on YOLOv3 effectively detect foreign objects debris, and has good accuracy and robustness.
Topics & Concepts
RunwayRobustness (evolution)Computer scienceObject detectionArtificial intelligenceResidualComputer visionFlight safetyDebrisFeature extractionSpace debrisFeature (linguistics)EngineeringPattern recognition (psychology)AeronauticsGeologyAlgorithmGeographyGenePhilosophyArchaeologyBiochemistryLinguisticsChemistryOceanographyAdvanced Neural Network ApplicationsInfrastructure Maintenance and MonitoringAutonomous Vehicle Technology and Safety