Removal and Selection: Improving RGB-Infrared Object Detection via Coarse-to-Fine Fusion

Object detection in visible (RGB) and infrared (IR) images has been widely applied in recent years. Leveraging the complementary characteristics of RGB and IR images, the object detector provides reliable and robust object localization from day to night. Most existing fusion strategies directly input RGB and IR images into deep neural networks, leading to inferior detection performance. However, the RGB and IR features have modality-specific noise, these strategies will exacerbate the fused features along with the propagation. Inspired by the mechanism of the human brain processing multimodal information, in this paper, we introduce a new coarse-to-fine perspective to purify and fuse two modality features. Specifically, following this perspective, we design a Redundant Spectrum Removal module to coarsely remove interfering information within each modality and a Dynamic Feature Selection module to finely select the desired features for feature fusion. To verify the effectiveness of the coarse-to-fine fusion strategy, we construct a new object detector called the Removal and Selection Detector (RSDet). Extensive experiments on three RGB-IR object detection datasets verify the superior performance of our method.