InfraNet: Quality-Aware RGB Guidance for Infrared Object Detection

Robust object detection under adverse visual conditions re-mains a long-standing challenge for multi-modal perception systems. Ex-isting 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 becomesunreliable or unavailable. In this case, we propose InfraNet, an IR-centric quality-aware framework that regulates RGB guidance duringtraining and supports flexible RGB–IR or IR-only deployment. InfraNetemploys an asymmetric architecture where the primary IR pathway ex-tracts multi-scale infrared features for predictions, while the auxiliaryRGB pathway provides reliability-controlled supervisory signals. Thecore of InfraNet is QualGate, a quality-aware fusion module that learnsa task-oriented control signal to suppress unreliable RGB guidance andcompensate IR features during cross-modal training. Built upon InfraNet,we design two architectural variants: a lightweight IR-only architectureInfraNet-IR and an RGB–IR architecture InfraNet-RGB-IR. Our methodis evaluated through extensive experiments on four benchmark datasets(LLVIP, FLIR-Aligned, M3 FD, and DroneVehicle), showing strong orcompetitive accuracy in challenging low-light and adverse weather con-ditions. Notably, InfraNet maintains high efficiency in IR-only inference,making it both accurate and computationally efficient.