MFDA-YOLO: A multiscale feature fusion and dynamic alignment network for UAV small objects detection.

ABSTRACT: Standard detectors such as YOLOv8 face significant challenges when applied to aerial drone imagery, including extreme scale variations, minute targets, and complex backgrounds. Their generic feature fusion architecture is prone to generating false positives and missing small objects. To address these limitations, we propose an improved MFDA-YOLO model based on YOLOv8. The model introduces an Attention-based Intra-scale Feature Interaction (AIFI) module in the backbone network to enhance high-level feature interactions, improve the adaptation to multi-scale targets, and strengthen feature representation. In the neck network, we design the Drone Image Detection Pyramid (DIDP) network, which integrates a space-to-depth convolution module to efficiently propagate multi-scale features from shal

SUBMITTER: Tian D 

PROVIDER: S-EPMC12680328 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature