Aerial Image Object Detection Based on RGB-Infrared Multibranch Progressive Fusion

In RGB-infrared aerial image object detection, fully utilizing the advantages of both RGB and infrared images for effective detection is a key challenge in this field. In response to the challenges outlined, an RGB-infrared multibranch progressive feature fusion method for object detection in aerial images is proposed. Specifically, since accurate detection of objects in aerial images usually requires extensive context information, considering the connection between objects and backgrounds, the global-local synergistic attention (GLSA) is constructed. To mitigate the impact of feature domain differences between RGB and infrared images during the complementary information filtering process, a multimodal complementary information filter (MCIF) for RGB and infrared images is designed on the basis of GLSA. To fully exploit the advantages of the two types of model images, a multibranch progressive feature fusion network is designed. The multibranch progressive feature fusion network progressively fuses the enhanced features from the GLSA with the filtered complementary information by the MCIF, resulting in a final fused feature that integrates the rich visual details of RGB images with the distinctive physical characteristics of infrared images. Results against publicly released datasets demonstrate that the proposed method achieves state-of-the-art detection performance.