MF-YOLO: High-Resolution Weighted Feature Fusion with Global-Local Aggregation for Small-Object Detection in UAV Imagery
TL;DR
MF-YOLO improves small-object detection at the cost of substantially greater computation, and introduces a stride-4 P2 prediction scale, applies Global-to-Local Spatial Aggregation independently to C2–C5 before neck fusion, and connects P2–P5 through a 64-channel Bidirectional Feature Pyramid Network (BiFPN).
Abstract
Small-object representations in unmanned aerial vehicle (UAV) imagery are easily degraded by repeated downsampling, crowded occlusion, and background clutter. We present MF-YOLO, an accuracy-oriented detector based on YOLO11n. It introduces a stride-4 P2 prediction scale, applies Global-to-Local Spatial Aggregation (GLSA) independently to C2–C5 before neck fusion, and connects P2–P5 through a 64-channel Bidirectional Feature Pyramid Network (BiFPN). The contribution lies in the task-specific organization and controlled evaluation of these established components. Across three random seeds, MF-YOLO achieved 38.318 ± 0.378% mean average precision at an intersection-over-union threshold of 0.5 (mAP@0.5) and 22.658 ± 0.239% mAP@0.5:0.95 on the validation split, exceeding YOLO11n by 4.264 and 2.694 percentage points, respectively. In COCO-style scale evaluation, average precision for small objects (APS) increased from 8.700 ± 0.092% to 12.802 ± 0.115%, a relative gain of 47.2%. The parameter count decreased from 2.584 M to 2.244 M, whereas giga floating-point operations (GFLOPs) increased from 6.323 to 14.365. On an RTX 4080 SUPER, 32-bit floating-point (FP32) network-forward throughput decreased from 144.22 to 70.50 frames per second (FPS). MF-YOLO therefore improves small-object detection at the cost of substantially greater computation.