Real-Time Recognition of Airport Surfaces and Horizontal Markings for Airside Driver Assistance: Model Comparison and Embedded Feasibility

Abstract

Airside vehicles operate close to aircraft, service equipment and safety-critical surface markings, yet driver-assistance functions developed for road traffic do not directly transfer to airport aprons, taxiways and service roads. This article presents a vision-based driver-assistance and warning prototype for recognizing airport surface types and horizontal markings in video recorded at Poznan Airport. Two manually annotated segmentation datasets were prepared from GoPro HERO8 video acquired from a vehicle perspective: a four-class surface dataset covering asphalt, concrete, paving blocks and grass, and a three-class marking dataset covering red, white and yellow lines. The study compares You Only Look Once (YOLO) variants YOLOv8 and YOLOv11 with U-Net, DeepLabV3 and SegFormer under a common 512-by-512 input resolution and evaluates both model-level quality and complete video-application behavior. For semantic segmentation, SegFormer achieved the highest validation results, with Intersection over Union (IoU)/Dice of 0.7657/0.8624 for surfaces and 0.8852/0.9380 for markings. Among YOLO models, YOLOv8m obtained the highest surface mean average precision at an IoU threshold of 0.50 (mAP@50) of 0.7847, whereas YOLOv8s obtained the highest marking mAP@50 of 0.8449. On video recordings, paired YOLO configurations processed approximately 15–16 frames per second (FPS) on a personal computer (PC), while U-Net, DeepLabV3 and SegFormer processed approximately 10–11 FPS. A YOLOv8n pair compiled for Raspberry Pi 5 with Raspberry Pi AI HAT+ Hailo-8 reached 10.05 detection FPS and 18.15 processing FPS without GUI rendering. Under the adopted evaluation protocol, SegFormer achieved the highest mask quality in the conducted comparison, while the paired YOLOv8n configuration demonstrated embedded throughput feasibility.