Co-D²: Collaborative Detection and De-ghosting with Radar-guided Infrared Perception

Infrared (IR) imaging is vital for perception under low-light and adverse weather conditions, yet its reliability is often undermined by ghost artifacts caused by specular reflections. These artifacts lead to false detections and hinder accurate scene understanding, particularly in safety-critical applications such as autonomous driving. Millimeter-wave (mmWave) radar offers motion-aware and geometry-consistent priors that hold significant potential for mitigating such artifacts. However, existing fusion methods struggle to reconcile the conflicting goals of object detection and ghost suppression. To address this challenge, we propose Co-D², a Collaborative Detection and De-ghosting framework that integrates radar-derived structural cues into infrared perception. The framework employs a Radar informed Cross-task Feature Separator (RCFS) to disentangle features for detection and restoration, coupled with a dual-branch architecture and an uncertainty-aware loss to balance the two tasks. We further establish the first benchmark for infrared ghost removal. Extensive experiments on real-world datasets demonstrate that Co-D² achieves superior performance in both detection and artifact suppression, highlighting its effectiveness for robust multimodal perception in complex environments.