Abstract: Since targets in remote sensing images are relatively small and easily affected by illumination, weather, and
other factors, deep-learning based target detection methods from single modality remote sensing images suffer from
low accuracy. However, the image information between different modalities can enhance each other to improve the
performance of target detection. Therefore, based on RGB and infrared images fusion, we proposed a balanced
multimodal depth model (BMDM) for multimodal small target detection from remote sensing images. As opposed to
simple element-wise summation, element-wise multiplication, and concatenation to fuse the feature information of the
two modalities, we designed a balanced multimodal feature method to enhance target features to make up for the
shortcomings of single modal information. We first extracted low-level features from RGB and infrared images,
respectively. Secondly, we fused the feature information of the two modalities and extracted deep-level features.
Thirdly, we constructed a multimodal small target detection model based on the one-stage method. Finally, the
effectiveness of the proposed method was verified by the experimental results of multimodal small target detection
performed on the public dataset VEDAI of remote sensing images.

Key words:  remote sensing images, balanced multimodal deep model, small target detection, fusion, VEDAI dataset