UAV-Landing Inclination Dataset: Enabling Inclination-Aware Surface Detection from UAV

Unmanned Aerial Vehicles (UAVs) has emerged as a transformative tool for 3D reconstruction, offering diverse applications in urban planning, infrastructure monitoring, and emergency response. This work introduces a combination of synthetic and real-world visual image dataset for estimating inclination of surfaces from UAV and is termed as UAV-Landing Inclination Dataset (UAV-LID). This work also proposes an ensemble deep learning architecture that carries out detection of possible landing surfaces and their inclination angle estimation. The surface detection architecture uses YOLOv7 module for surface detection while the inclination angle estimator uses different backbone architectures to estimate inclination. The dataset consists of visual images of different kinds of possible surfaces at different heights and inclination angles. Different backbones such as VGG16, EfficientNet, and ConvNext based architectures have been experimented here for the task of inclination estimation, of which the EfficientNet based architecture shows promising performance. Experimental results show that deep learning-based networks can be used effectively for this purpose and in future, can be extended for landing of UAV on slanted surfaces directly.