Biological eagle eye-based method for change detection in water scenes

Abstract Change detection (CD) is an important vision task for autonomous landing of unmanned aerial vehicles (UAV) on water. High-density photoreceptors and lateral inhibition mechanisms have inspired a novel biologic computational method based on structure and properties in eagle eyes as proposed for change detection. We call this method “STabCD,” which ensures spatiotemporal distribution consistency to achieve foreground acquisition, noise reduction, and background adaptability. Therefore, our proposed model responds strongly to object information and suppresses noise and wave textures. Then, we present a cloning method to simulate water scenes and collect a new synthetic dataset (called “Synthetic Boat Sequence”) for UAV vision research. Besides, we utilize synthetic datasets and corresponding real datasets to conduct change detection experiments. The experimental results indicate that: 1) the STabCD model achieves the best results in real or synthetic water landing scenes; and 2) change detection models for UAV can be quantitatively analyzed and tested under challenging synthetic scenarios.