LAUSR

With practical applications such as environment surveillance, agricultural production, and disaster assessment, accurate object detection in remote sensing images is in high demand. Precise detection of object instances in remote sensing images remains considerably challenging due to dense instance stacking, large-scale variations, and complex backgrounds. To solve the mentioned issues, a novel global context-weaving network (GCWNet) is developed for object detection in remote sensing images. We propose two novel modules for feature extraction and refinement, which include the global context aggregation module (GCAM) and the feature refinement module (FRM). GCAM assembles a global context with high-level and low-level features through feature weaving, which facilitates dense object detection. Meanwhile, FRM convolves multiple receptive fields by combining different branches, thereby further refining the features and improving the feature distinction at different scales. Furthermore, we design to alleviate the sample imbalanced problem during training using focal loss and balanced L1 loss to improve object classification and regression, respectively. The experimental results indicate that GCWNet achieves superior performance in object classification and localization on the DOTA-v1.5 dataset, which illustrates the superiority of GCWNet.