Efficient cross-modality feature interaction for multispectral armored vehicle detection

Detecting armed vehicles from a UAV platform is challenging due to the complexity of ground environment. This paper presents a dual-stream multispectral armored vehicle detection method to tackle this problem. First, considering that there is a paucity of datasets containing multispectral armored vehicle images, a multispectral armored vehicle detection dataset is constructed for this study. The dataset consists of 5853 pairs of RGB and infrared images, featuring a total of 15,878 instances of armored vehicles. Then, a cross-modal feature interaction module is designed to enable efficient feature interaction between multispectral images. This module uses the cross-modal channel-wise feature difference method to model the channel differences between the two modal features and obtains the cross-modal channel difference matrix. The cross-modal channel difference matrix is then employed to extract the unique features of the two modal features, allowing for efficient cross-modal feature interaction by complementing each other's unique features. Experiment results demonstrate that the proposed model has excellent detection performance and is capable of coping with various challenges brought by complex ground environments.