Research on Foreign Object Debris Detection in Airport Runway Based on Semantic Segmentation
Qiang Gao, Ruifeng Hong, Yutong Chen, Jiaxing Lei
Abstract
Semantic segmentation based on deep learning has very broad application scenarios in the field of computer vision. DeepLab v3+ is currently a widely used semantic segmentation framework. This paper uses the DeepLab v3+ model in TensorFlow to semantically segment foreign objects on the airport runway. Experimental results show that the semantic segmentation model based on DeepLab v3+ can accurately classify foreign objects at the pixel level, which effectively improves the accuracy of foreign object detection and reduces the risk of accidental aircraft take-off and landing.
Topics & Concepts
Computer scienceRunwaySegmentationSemantics (computer science)Artificial intelligenceObject (grammar)Image segmentationField (mathematics)Object detectionComputer visionNatural language processingMathematicsProgramming languageHistoryPure mathematicsArchaeologyAdvanced Neural Network ApplicationsInfrastructure Maintenance and MonitoringAutonomous Vehicle Technology and Safety