SBF-YOLO: A Small Object Detection Network for Aerial Scene

In response to the challenges of small object detection in aerial images, such as the small size of targets, low image resolution, and high similarity between the background and targets, an improved aerial small object detection model based on YOLOv10, named SBF-YOLO, is proposed. First, a new pyramid structure, MBSFPN, is applied to the neck layer to optimize its ability to fuse multi-scale features. Next, large-scale feature extraction modules are added at the base of the feature pyramid, combining SPDConv and CSP-OKM, to improve the edge details of small objects. Additionally, dynamic adaptive modules improve upsampling and downsampling via AKConv and DySample, enabling the model to better capture fine-grained features and spatial relationships, further enhancing detection performance. Experiments on the VisDrone2019 dataset show that SBF-YOLO achieves 14.6% and 16.0% improvements in mAP@0.5 and mAP@0.5: 0.95, respectively, over the baseline algorithm, demonstrating the effectiveness and superiority of this algorithm in aerial small object detection tasks. Our code is available on GitHub¹¹https://github.com/081degrees/MBT-YOLO.git.