EBFF-YOLO: enhanced bimodal feature fusion network for UAV image object detection

Existing methods for fusing visible light and infrared features often focus on separating objects from backgrounds. In unmanned aerial vehicle (UAV) images, background areas typically contain multiple types of small objects, and the dispersed nature of these objects reduces the effectiveness of feature fusion, leading to phenomena such as missed detections and false alarms when dealing with aerial small objects. This paper introduces a bimodal feature fusion network named EBFF-Net, designed to address the issue of UAV image object detection in low-visibility environments. In this study, a shallow learning network is utilized to extract complementary features, and the Parallel Shallow Feature Fusion (PSFF) method is designed to extract and fuse bimodal features. Additionally, a reconfigurable structure with diverse branch blocks is introduced to the bottleneck layer to better capture feature information without increasing the computational burden during inference. Furthermore, leveraging the geometric properties of two-dimensional rectangles and based on an adaptive weighting algorithm, a novel localization loss function is developed. Subjective and objective testing on the VEDAI, DIOR, and VAID datasets validate the efficacy and lightweight.