Joint airport runway segmentation and line detection via multi-task learning for intelligent visual navigation

This paper presents a novel multi-task learning framework for joint airport runway segmentation and line detection, addressing two key challenges in aircraft visual navigation: (1) edge detection for sub-5 %-pixel targets and (2) computational inefficiencies in existing methods. Our contributions include: (i) ENecNet, a lightweight yet powerful encoder that boosts small-target detection IoU by 15.5 % through optimized channel expansion and architectural refinement; (ii) a dual-decoder design with task-specific branches for area segmentation and edge line detection; and (iii) a dynamically weighted multi-task loss function to ensure balanced training. Extensive evaluations on the RDD5000 dataset show state-of-the-art performance with 0.9709 segmentation IoU and 0.6256 line detection IoU at 38.4 FPS. The framework also demonstrates robust performance (0.9513–0.9664 IoU) across different airports and challenging conditions such as nighttime, smog, and mountainous terrain, proving its suitability for real-time onboard navigation systems.