Improving RGB-Infrared Object Detection by Reducing Cross-Modality Redundancy

Abstract

In the field of remote sensing image applications, RGB and infrared image object detection is an important technology. The object detection performance can be improved and the robustness of the algorithm will be enhanced by making full use of their complementary information. Existing RGB-infrared detection methods do not explicitly encourage RGB and infrared images to achieve effective multimodal learning. We find that when fusing RGB and infrared images, cross-modal redundant information weakens the degree of complementary information fusion. Inspired by this observation, we propose a redundant information suppression network (RISNet) which suppresses cross-modal redundant information and facilitates the fusion of RGB-Infrared complementary information. Specifically, we design a novel mutual information minimization module to reduce the redundancy between RGB appearance features and infrared radiation features, which enables the network to take full advantage of the complementary advantages of multimodality and improve the object detection performance. In addition, in view of the drawbacks of the current artificial classification of lighting conditions, such as the subjectivity of artificial classification and the lack of comprehensiveness (divided into day and night only), we propose a method based on histogram statistics to classify lighting conditions in more detail. Experimental results on two public RGB-infrared object detection datasets demonstrate the superiorities of our proposed method over the state-of-the-art approaches, especially under challenging conditions such as poor illumination, complex background, and low contrast.