Abstract: To address issues such as missed detections and false detections caused by characteristics like scattered object distributions, significant size variations, and indistinct features in UAV  images, this paper proposes an improved aerial small object detection model based on YOLOv8n, named ASOD-YOLO. Firstly, the feature fusion network is redesigned: the top-down part of the original feature pyramid structure is replaced with a low-level information distribution (Low-GD) structure. This modification reduces feature loss while enhancing the information fusion ability across different scales. Secondly, the original 20×20 large-object detection head is replaced with a 160×160 small-object detection head to improve the detection capability for small objects. Additionally, the multi-scale cross-layer connections are optimized to provide the detection head with richer semantic information. Meanwhile, a fast fourier convolution module(FFCBlock) is introduced to reduce the loss of small-object information after downsampling and enhance the ability to extract global contextual information. Experimental results on the VisDrone aerial small-object dataset show that, compared with the baseline YOLOv8n model, the ASOD-YOLO model achieves  a 4.1 percentage points improvement in the mAP@50 metric and  2.3 percentage points improvement in the mAP@50:95 metric, with a single-image processing time of only 6.8 ms. These results demonstrate that the proposed ASOD-YOLO model can effectively accomplish the task of aerial small-object detection.

Key words: deep learning, aerial small object detection, fast Fourier convolution, feature fusion