M2C: Matchability-Aware Mutual-Consistency Fusion for RGB–Infrared Object Detection

We propose M2C, a matchability-aware mutual-consistency fusion method for RGB–infrared object detection. For each infrared query, M2C augments the local RGB candidate set with an explicit no-match state, measures ambiguity through the concentration of the conditional candidate distribution, and verifies the forward correspondence using reverse matchability and forward–reverse soft-offset agreement. The resulting confidence serves as a continuous evidence-admission signal that regulates RGB routing and gates target-aware cross-stage updates to the infrared stream. This formulation distinguishes finding a relative local match from determining whether its RGB evidence is reliable enough for fusion, while requiring neither cross-modal correspondence annotations nor an additional cycle-consistency loss. Experiments on DroneVehicle and LLVIP demonstrate the effectiveness of M2C across aerial and pedestrian scenes. On DroneVehicle, M2C achieves 63.67% ${\mathrm{mAP}}_{50:95}$, outperforming a controlled LocalMatch baseline by 0.62 percentage points. Under RGB replacement, the improvement increases to 5.45 points, while the clean-to-degraded performance drop decreases from 14.98 to 10.14 points. These results show that structured correspondence reliability enables more selective and robust RGB–infrared interaction.