GACFNet: A global attention cross-level feature fusion network for aerial image object detection

Abstract

Real-time object detection in aerial images is challenging, primarily due to small and densely packed objects, accompanied by significant scale variations. Previous methods have addressed these issues by employing fusion structures similar to feature pyramid networks. However, these fusion structures overlook the complementary relationship between feature information from non-adjacent layers. To tackle this, we propose a global attention cross-layer feature fusion network (GACFNet). Firstly, we design a global attention cross-layer feature fusion (GACF) module, which obtains global information by fusing features at different scales, using the attention mechanism to highlight foreground information in the global feature map. Additionally, we connect the global attention feature map with other layers to establish correlations between non-adjacent layers. Secondly, a large-kernel separable pooling pyramid fusion (LKSPPF) module is proposed to capture a wider receptive field and enhance context information. Thirdly, to better preserve small object information in low-resolution feature maps, we improve the cross-stage partial fusion module (C2f) of the baseline using a deformable convolution technique (DCNv2). Finally, we design a hybrid regression function (NGIoU loss) to improve object localization and sample allocation in aerial images while accelerating model convergence. Extensive experiments were conducted on three publicly available aerial image datasets. The experimental results show that the method significantly improves the accuracy of object detection in aerial images. The average precision ($A{P}_{50}$) of the three datasets reaches 52.7%, 81.8%, and 33.0%, respectively, while a real-time performance of 69.9 frames per second is achieved. The code will be available online https://github.com/JSJ515-Group/GACFNet/.