DUCAF-Net : An Object Detection Method for UAV Imagery.

This paper proposes an object detection method called "Down Up Coordinate Attention Fusion model (DUCAF-Net)" to address the challenges of object density and complex backgrounds in aerial images captured by drones. DUCAF-Net integrates coordinate attention at different resolutions, aiming to learn spatial coordinate information from feature maps at various resolutions, enhancing the expression capability of spatial features, and simultaneously reducing the diffusion of features from small dense objects and the phenomenon of feature coupling. DUCAF-Net introduces deformable convolutions and transposed convolutions, and designs an upsampling module to increase the receptive field of feature maps, better capturing the details of target features, thus improving the object detection performance. The AP score on the VisDrone2019 test set is 22.9. DUCAF-Net demonstrates satisfactory performance in medium-scale object detection and also performs well in small-scale object detection. The experimental results show that DUCAF-Net's performance is delightful. [ABSTRACT FROM AUTHOR]