CF-YOLO for small target detection in drone imagery based on YOLOv11 algorithm

Images captured from a drone's perspective are significantly impacted in terms of target detection algorithm performance due to the notable differences in target scales and the presence of numerous small target objects lacking detailed information. This paper proposes a Remote Sensing Small Target Detector (CF-YOLO) based on the YOLOv11 model to address the challenges of small target detection. Firstly, addressing the issue of small target information loss that may arise from hierarchical convolutional structures, we conduct in-depth research on the Path Aggregation Network (PAN) and innovatively propose a Cross-Scale Feature Pyramid Network (CS-FPN). Secondly, to overcome the problems of positional information deviation and feature redundancy during multi-scale feature fusion, we design a Feature Recalibration Module (FRM) and a Sandwich Fusion Module. We advocate for initial feature fusion through the FRM module, followed by feature enhancement using the Sandwich module. Finally, we optimize and reconstruct the model using the RFAConv module and LSDECD detection head. Experiments show that on the public VisDrone dataset, TinyPerson dataset, and HIT-UAV dataset, CF-YOLO improves the mAP50 by 12.7%, 10.1%, and 3.5%, respectively, compared to the baseline model. Compared to other methods, CF-YOLO demonstrates superior performance.