Information balance network for multiscale object detection in remote sensing imagery

Remote sensing images are often characterized by complex background environments, significant scale variations, and high visual similarity. Most remote sensing object detection algorithms have adopted the structure of a feature pyramid with coupled heads. However, this design suffers from weak feature fusion capabilities and limited algorithm flexibility. To tackle these issues, we propose a remote sensing object detection algorithm based on the improved YOLOv7-tiny. First, we introduce the spatial information integration module to address the imbalance between semantic and positional information during feature extraction. Second, the GD-Neck, based on a gather-and-distribute mechanism, is adopted to mitigate the issue of information loss during feature fusion in the neck network. Finally, the original coupled detection head is replaced with a re-designed multi-channel decoupled head to solve the problem of performance degradation due to the shared network layer of the coupled detector head limiting the flexibility of the algorithm. Validated on the public datasets RSOD and NWPU VHR-10, the experimental results show that the algorithm in this paper obtains an accuracy improvement of 4.8% and 3.7%, respectively, and realizes high-performance remote sensing object detection.