Fast dual-branch stitching and synchronous detection framework based on shared backbone

In low-altitude remote sensing for real-time inspection, strong parallax, scale variations, and local distortions make the traditional “stitch-then-detect” serial paradigm prone to feature redundancy and error cascading, and make it difficult to unify global and local geometric transformations, which in turn limits speed and weakens robustness. To address this, we propose Fast Stitching and Detection Network (FSDNet), a fast dual-branch stitching and synchronous detection framework built on a shared backbone. The framework adopts a pretrained detection backbone as a unified encoder and embeds an attention-guided local context correlation module in the stitching branch to explicitly regress fine-grained geometric flow fields from shared features. In addition, two collaborative branches are designed for estimating the transformation fields: a global homography branch for coarse alignment and a local thin-plate spline branch for fine alignment. These are combined with transformation-guided detection box rectification and intensity-adaptive fusion to enhance geometric-semantic consistency. Experiments on UDIS-D and Warped AU-AIR demonstrate that, while maintaining high-quality stitching, the proposed method improves Frames per Second (FPS) by about 66% compared with typical serial baselines and achieves superior object detection performance on Warped AU-AIR, validating the efficiency and practicality of the approach.