Multimodal Object Detection of UAV Remote Sensing Based on Joint Representation Optimization and Specific Information Enhancement

With the development of Earth observation technology, it becomes easier and easier to acquire multi-modal image data at the same time. To improve the performance of multi-modal remote sensing detection algorithm, a new fusion feature optimization detection network (FFODNet) is proposed. The method is designed to solve the problem of performance degradation caused by the unreliability of single modal data in multi-modal remote sensing data. The key to obtain high quality fusion features from multi-modal data with interference is to suppress single modal redundant features and fully integrate multi-modal features. The proposed method mainly includes two improvements. Firstly, a novel joint expression optimization module (JEOM) is designed to enhance the target features and suppress the redundant and interference features that affect the fusion effect. Additionally, we propose a novel specific information enhancement module (SIEM) to further enhance the discriminative feature information of targets within each modal image. Experiments on DroneVehicle dataset show that our proposed method is state-of-the-art on this dataset.