Small Object Detection Algorithm Based on Improved Attention Mechanism and Feature Fusion of YOLOv8

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YOLOv8-FE achieves an increase of 8.3% in mAP50 compared to YOLOv8n, demonstrating marked performance enhancement.
The innovative RFAC-SPD downsampling module captures critical features of small objects, reducing information loss.
Generalization experiments show mAP50 improvements of 2.7% on DOTA and 6.8% on Caltech Pedestrian datasets.
Real-time inference speed of 77 frames per second validates the algorithm's effectiveness for practical applications.

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Proposed YOLOv8-FE algorithm improves small object detection performance, indicating significant efficiency gains.

Fang et al. (2025) studied this question.

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