Small target detection algorithm for UAV images based on FD-YOLOv8s

Aiming at the existing UAV aerial image target detection algorithms with lower detection precision and more complex models, a small target detection algorithm is proposed to improve YOLOv8s. First, a new C2f-Faster module is constructed in the feature extraction network using the Partial Convolution (PConv) of the FasterNet Block module, this approach optimizes the preservation of feature information related to small targets while simultaneously minimizing network parameters and computational overhead. Secondly, a small target detection header is introduced in the Neck part to further improve the small target detection capability. This improved algorithm, FD-YOLOv8s, is evaluated on the VisDrone2019 dataset and achieves a detection accuracy of 41.1%, which improves the detection accuracy by 2.6 percentage points and decreases the parameter count by 1.8 points in comparison to the YOLOv8s algorithm. Better detection performance is also obtained compared to other mainstream target detection methods.