A method for detecting small objects in remote sensing images based on feature enhancement and scale-adaptive optimization in complex scenarios

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

To address the challenges of small object detection in remote sensing images, such as susceptibility to background noise and difficulties in feature extraction, this paper proposes a method for detecting small objects in remote sensing images based on feature enhancement and scale-adaptive optimization under complex scenarios. First, we constructed a bidirectional complementary feature fusion network, employing a bidirectional feature fusion mechanism to enhance the interaction between shallow-level details and deep-level semantic features, thereby enabling more effective multi-scale feature fusion. Second, we developed a Multi-scale Local-Global Attention (MLGA) module that uses multi-scale pooling to capture both local details and global context, and employs dynamic weights for adaptive feature fusion, effectively suppressing background noise and highlighting the features of small objects. In addition, we designed a scale-adaptive bounding box regression loss function (SAWIoU) that addresses the scale bias issue for small objects by introducing a scale scaling factor, thereby ensuring balanced optimization across objects of different scales and further improving localization accuracy. Finally, experimental results on two public remote sensing datasets DIOR and RSOD show that the proposed method achieved mAP50 scores of 0.885 and 0.971, respectively, significantly outperforming current mainstream detection models, with particularly notable improvements in small-object detection accuracy. Additionally, ablation studies further validate the effectiveness and robustness of this method in detecting small objects in remote sensing under complex backgrounds.