InfraNet: Quality-Aware RGB Guidance for Efficient Infrared Object Detection
Robust object detection under adverse visual conditions remains a long-standing challenge for multi-modal perception systems. Existing fusion-based methods typically require both RGB and infrared (IR) inputs, and treat them equally during both training and inference, which compromises their robustness when the RGB modality becomes unreliable or unavailable. In this case, we propose \textbf{InfraNet}, an IR-centric quality-aware framework that regulates RGB guidance during training and supports flexible RGB--IR or IR-only deployment. InfraNet employs an asymmetric architecture where the primary IR pathway extracts multi-scale infrared features for predictions, while the auxiliary RGB pathw...
| Authors | Zichao Feng, Haodong Zhu, Jingying Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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