Improved YOLOv5 algorithm for small object detection in UAV aerial imagery

Due to complex backgrounds, varying object scales, and many small objects in Unmanned Aerial Vehicle (UAV) imagery, existing algorithms often have low detection accuracy, especially for small objects. To tackle these issues, we propose an improved YOLOv5-based algorithm. A Backbone Feature Weighted Fusion (BFWF) module is introduced to extract and fuse multi-scale features from the backbone. These extracted features are fused with the original neck output through cross-neck connections, enhancing fine-grained features. The neck structure is adjusted to improve the diversity and weighting of shallow fine-grained features. An Adaptive Spatial Feature Fusion (ASFF) module dynamically fuses features at different levels in the Path Aggregation Network (PANet), improving small object detection accuracy. By replacing the original loss function with the Efficient Intersection over Union (EIoU) loss function, we achieve more precise learning of small object dimensions across multi-scale scenarios. Experiments on the VisDrone2021 dataset show that the improved algorithm increases mAP50 by 3.7%, mAP75 by 2.5%, and mAP50:95 by 2.4% compared to the original YOLOv5 algorithm. This significantly enhances detection performance, making it more suitable for UAV imagery.