Efficient method for detecting targets from remote sensing images based on global attention mechanism

Remote sensing image target detection provides an effective and accurate data analysis tool for many application areas. Due to complex backgrounds, large differences in target scales, and missed detection of small targets, remote sensing image target detection is challenging. In order to enhance the model's understanding of the global information of remote sensing images, this paper proposes the GFA module. This module can establish the global contextual connection of remote sensing images to provide rich context to help understand the complex scene and background in which the target is located, without being limited to local information. Additionally, it focuses on channel information for enhanced target feature extraction. For the purpose of alleviating the serious imbalance in foreground–background samples that is present in single-level target detection models. The loss function is reconstructed based on focal loss by redefining the balance factor α and focus factor γ, so that it can be dynamically adjusted during network training. Meanwhile, EIoU is used to further enhance the bounding box regression capability. Affine transformations were also used to augment the dataset in order to assist the model in adjusting to real-world situations. The proposed method is experimentally validated on the publicly available HRRSD dataset. In comparison with YOLO v5, the mAP of the detection results improved by 2.7%. Compared with YOLO v8 and YOLO v10, the mAP improved by 3.2% and 3.3%. The model achieves an FPS of 40.1, an optimal balance between speed and accuracy. Further, experiments are conducted using the NWPU VHR-10 dataset and the RSOD dataset, both of which demonstrated that the proposed method outperforms other target detection methods and improves remote sensing target detection performance.