SFFEF-YOLO: Small object detection network based on fine-grained feature extraction and fusion for unmanned aerial images

Abstract

Unmanned aerial vehicles (UAVs) images object detection has emerged as a research hotspot, yet remains a significant challenge due to variable target scales and the high proportion of small objects caused by UAVs’ diverse altitudes and angles. To address these issues, we propose a novel Small Object Detection Network Based on Fine-Grained Feature Extraction and Fusion(SFFEF-YOLO). First, we introduce a tiny prediction head to replace the large prediction head, enhancing the detection accuracy for tiny objects while reducing model complexity. Second, we design a Fine-Grained Information Extraction Module (FIEM) to replace standard convolutions. This module improves feature extraction and reduces information loss during downsampling by utilizing multi-branch operations and SPD-Conv. Third, we develop a Multi-Scale Feature Fusion Module (MFFM), which adds an additional skip connection branch based on the bidirectional feature pyramid network (BiFPN) to preserve fine-grained information and improve multi-scale feature fusion. We evaluated SFFEF-YOLO on the VisDrone2019-DET and UAVDT datasets. Compared to YOLOv8, experimental results demonstrate that SFFEF-YOLO achieves a 9.9% mAP0.5 improvement on the VisDrone2019-DET dataset and a 3.6% mAP0.5 improvement on the UAVDT dataset.