基于改进 Double-Head RCNN 的无人机航拍图像小目标检测算法

The feature information of small targets in unmanned aerial vehicle aerial images is small and easily interfered with by noise, which leads to the high missed detection and false detection rates of existing algorithms. To address these issues, a small target detection algorithm based on an improved Double-Head region-convolutional neural networks（RCNN）for unmanned aerial vehicle aerial images was proposed. Transformer and deformable convolution networks (DCN) modules were introduced on the backbone network ResNet-50 to extract small target feature information and semantic information more effectively. A feature pyramid network（FPN） structure based on content-aware reassembly of features (CARAFE) was proposed to solve the problem that the small target information is interfered with by the background noise, and the feature information is lost in the process of feature fusion. The generation scale of Anchor was reset according to the characteristics of small target scale distribution in the region proposal network to further improve the small target detection performance. The experimental results on the VisDrone-DET2021 dataset show that the proposed algorithm can extract feature and semantic information of small targets with representational capacity more effectively. Compared with the Double-Head RCNN algorithm, the parameter quantity of the proposed algorithm increases by 9.73×10⁶, and the FPS loss is 0.6. However, AP, AP50, and AP75 increase by 2.6%, 6.2%, and 2.1% respectively, and APs increases by 3.1%.