Aerial RGB-infrared （IR） object detection has received increasing attention in remote sensing because visible and IR images provide complementary information under complex imaging conditions. Visible images preserve rich texture，color，and structural details，whereas IR images are less sensitive to low illumination and can highlight thermal targets at night or in low-light scenes. However，the contribution of each modality changes with the imaging factors involved，such as illumination，exposure status，texture clarity，target-background contrast，background clutter，and artificial light interference. To date，existing methods have improved detection performance through cross-modal alignment，feature interaction，attention reweighting，and multiscale fusion. Nevertheless，most of them still focus on feature-level fusion and lack explicit modality reliability modeling. In other words，they are unable to sufficiently estimate how reliable each modality is under the current imaging condition，or how much each modality should contribute to detection. While some language-guided and condition-aware methods introduce semantic cues into multimodal detection，they usually rely on coarse scene descriptions，category-level prompts，or additional large-model branches during inference. These strategies are insufficient for characterizing modality quality attributes that are directly related to detection reliability. They may also increase the deployment burden on computation-constrained aerial platforms. To address these issues，this paper proposes a modality reliability modeling method for aerial RGB-IR object detection. This modeling method transfers modality quality perception from a vision-language model to the detector during training，thus enabling adaptive multimodal fusion without additional large-model inference cost.

The proposed method consists of structured modality quality description，semantic prior distillation，and reliability-aware adaptive fusion. First，a modality quality attribute description dataset is constructed for UAV-oriented aerial scenes，thus providing structured supervision for modality reliability learning. Instead of using only category labels or coarse scene tags，the annotation scheme explicitly describes key imaging factors that affect detection performance in both modalities. For the RGB modality，the attributes are illumination condition，exposure status，texture clarity，artificial light interference，and background clutter. For the IR modality，the attributes are target-background contrast，boundary clarity，and background cleanliness. Second，a vision-language model is used to encode the modality quality descriptions and generate semantic priors related to RGB and IR reliability. These priors are used only during training. Here，the detector is guided to learn detection-oriented reliability representations through the combination of semantic distillation with attribute supervision. Thus，modality quality perception is internalized into the visual detection network. Third，a global-local adaptive fusion mechanism is designed on the basis of learned reliability representations. Global scene reliability captures the overall effectiveness of RGB and IR cues under the current imaging condition，while local spatial reliability further adjusts the modality contribution at different spatial positions. In this way，the detector can dynamically fuse RGB and IR features in accordance with scene- and region-level reliability. Given that semantic priors are used only during training，the proposed framework does not require additional text prompts，language branches，or large-model participation during inference.

Experiments are conducted on DroneVehicle and VEDAI，two public aerial RGB-IR object detection datasets. On DroneVehicle，the proposed method achieves 79.7% mAP@0.5 and 53.7% mAP@0.5：0.95. On VEDAI，it achieves 67.1% mAP@0.5 and 29.8% mAP@0.5：0.95. The method also shows strong robustness in challenging scenarios，especially under nighttime，low-light，and complex interference conditions. Ablation studies further verify the effectiveness of modality quality attribute modeling，semantic prior distillation，and joint global-local modality reliability modeling. Additionally，the proposed method maintains good inference efficiency because no extra-large-model branch is introduced during testing.

This paper presents a modality reliability modeling method for aerial RGB-IR object detection. To encode modality quality descriptions and provide semantic supervision，the proposed method uses a vision-language model only during training. The detector then learns reliability-aware representations through semantic distillation and attribute supervision. By jointly modeling global scene reliability and local spatial reliability，the detector can adaptively adjust the contributions of visible and IR modalities under varying imaging conditions. Finally，experimental results on DroneVehicle and VEDAI demonstrate that the proposed method improves detection accuracy and robustness，especially in nighttime，low-light，and cluttered scenes.