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.

With the rapid development of urban ground transportation, lane line detection is gradually becoming a major technological direction to help to realize safe vehicle navigation. However, lane line detection results may have incompleteness issues, such as blurry lane lines and disappearance of the lane lines in the distance, since the lane lines may be heavily obscured by vehicles and pedestrians on the road. In addition, low-visibility environments also pose a challenge for lane line detection. To solve the above problems, we propose a dynamic data augmentation framework based on imitating real scenes (DDA-IRS). DDA-IRS contains three data augmentation strategies that simulate different realistic scenes (i.e., shadows, dazzle, and crowded). In this way, we expand from a limited scene dataset to realistically fit multiple complex scenes. Importantly, DDA-IRS is a lightweight framework that can be integrated with a variety of training-based models without modifying the original model. We evaluate the proposed DDA-IRS on the CULane dataset, and the results show that the data-enhanced model outperforms the baseline model by 0.5% in terms of F-measure. In particular, the F-measure of the “Normal”, “Crowded”, “Shadow”, “Arrow”, and “Curve” achieve a 0.4%, 0.1%, 1.6%, 0.4%, and 1.4% improvement, respectively.