A Lightweight Direction-Aware Self-Supervised Monocular Depth Estimation Method for UAVs

Existing self-supervised methods have achieved significant success in ground-level autonomous driving scenarios, but applying them directly to Unmanned Aerial Vehicle (UAV) videos remains challenging. On the one hand, rapid pose changes in UAVs often lead to oblique-view imaging, making it difficult for conventional methods to handle the perspective distortion in oblique imagery. On the other hand, complex UAV viewpoints may cause depth blurring in low-texture regions. To address these challenges, we propose a lightweight self-supervised monocular depth estimation method for UAV scenarios. By utilizing a Dynamic Direction-Aware Module (DDaM), the network adaptively adjusts the sampling grid to correct distorted features during feature extraction, while enhancing its ability to capture features at different spatial locations. Furthermore, to mitigate the loss of spatial information caused by multiple downsampling operations, we integrate a Coordinate Attention Mechanism into the encoder. This mechanism captures features along two separate spatial axes, preserving the spatial coordinates of object boundaries. Our experiments demonstrate that the synergy between DDaM and the Coordinate Attention Mechanism enables the prediction of more accurate object boundaries and richer local details. To validate the effectiveness and practical applicability of the proposed method, we conduct experiments on both the MidAir synthetic dataset and the UAVid real-world dataset. The results show that, compared with current baseline methods, our approach maintains competitive performance while requiring the fewest parameters.