Abstract Automated analysis of UAV imagery is crucial for remote sensing but faces challenges like extreme scale variations, complex background clutter, and the vanishing of fine-grained details. To address these limitations, we propose MMAF-YOLO, a novel object detection network based on multi-scale mutual aggregation and fine-grained feature modulation. First, we design a Multi-Scale Mutual Aggregation Fusion (MMAF) block in the backbone to replace the standard C3k2 module. This effectively captures complementary multi-scale information and mitigates feature loss for small targets. Second, we devise a Fine-Grained Feature Modulation Neck incorporating a Modulation Fusion Module (MFM) and an Efficient Up-Convolution Block with Channel Shuffle (EUCB-CS) to resolve semantic misalignment and repair structural distortions during upsampling, supplemented by a high-resolution P2 detection head. Third, a joint optimization strategy combining Inner-IoU and Scale-based Dynamic Loss (SDLoss) is employed to stabilize regression and improve recall. To satisfy diverse deployment requirements, we develop a scalable model series consisting of four variants (n, s, m, l). Comprehensive evaluations conducted on the VisDrone2019 dataset demonstrate that MMAF-YOLO surpasses the YOLO11 baseline across all model scales. Specifically, the MMAF-YOLO-s variant improves mAP@0.5 by 5.0% over YOLO11-s with reduced parameters. Furthermore, evaluations on the HIT-UAV (infrared) and HazyDet (adverse weather) datasets confirm the model’s robust generalization capabilities across diverse remote sensing modalities and extreme environmental conditions.
Yang et al. (Fri,) studied this question.