A Robust Airport Runway Detection Network Based on R-CNN Using Remote Sensing Images

Autonomous vision-based taxi, takeoff, and landing systems are gaining popularity in fixed-wing commercial, military, and unmanned aerial vehicles presenting new technical and regulatory challenges. With ongoing advances in deep learning, machine learning, and big data management, the dangers of takeoff and landing operations have greatly decreased, and with less dependence on simultaneous and complex controllers. However, in a real-world scenario, airport runway detection for takeoff and landing operations is still deemed challenging, due to the vast amount of contextual information, complex backgrounds, and dangerous geographical scenarios. In this work, we propose an end-to-end airport runway detection network for automatic runway detection and localization centered on a two-stage region-based convolutional neural network that could significantly improve navigation efficiency and dependence on complex control systems. The first stage is an enhanced region proposal network with domain-specific constraints added, which automatically learns and extracts multiscale (high, mid, and low-level) features using a deep convolutional neural network and filters low-quality positive samples for robust runway detection. The second stage classifies the candidate regions and determines the precise localization of predictions using an enhanced anchor refinement and regression algorithm with a nonmaximum suppression function for bounding box optimization. Transfer learning and data augmentation techniques are used to propagate relevant domain-specific semantic and context information during the training phase to handle the problem of imbalances in the dataset classes. The results show the effectiveness of our algorithmic contributions as well as the benefits of the specific enhancements, in terms of, improved efficiency and accuracy of runway detection in remote sensing images.