DetailCaptureYOLO: Accurately Detecting Small Targets in UAV Aerial Images

Abstract

Unmanned aerial vehicle aerial imagery is dominated by small objects, obtaining feature maps with more detailed information is crucial for target detection. Therefore, this paper presents an improved algorithm based on YOLOv9, named DetailCaptureYOLO, which has a strong ability to capture detailed features. First, a dynamic fusion path aggregation network is proposed to dynamically fuse multi-level and multi-scale feature maps, effectively ensuring information integrity and richer features. Additionally, more flexible dynamic upsampling and wavelet transform-based downsampling operators are used to optimize the sampling operations. Finally, the Inner-IoU is used in Powerful-IoU, effectively enhancing the network’s ability to detect small targets. The neck improvement proposed in this paper can be transferred to mainstream object detection algorithms. When applied to YOLOv9, AP50, mAP and AP-small were improved by 8.5%, 5.5% and 7.2%, on the VisDrone dataset. When applied to other algorithms, the improvements in AP50 were 5.1%–6.5%. Experimental results demonstrate that the proposed method excels in detecting small targets and exhibits strong transferability. The codes are at: https://github.com/SFXSunFengXi/DetailCaptureYOLO.