Low-altitude UAV RGB-T small-object detection is challenged by unequal modality reliability and the progressive attenuation of small-object evidence. To address these issues, this paper proposes Modality-Dominant Controlled Fine-Tuning with Hierarchical Small-Object Modeling (MDCFT-HSM), an ordered feature-flow framework for RGB-T detection. Based on dataset-level single-modality performance, MDCFT selects an initial protected branch and introduces auxiliary-modality information through zero-initialized residual mappings at the stem, P3, P4, and P5 stages, thereby limiting interference from degraded auxiliary features. Within this controlled feature flow, the High–Low Frequency Detail Enhancement module (HLFDE) preserves shallow boundary, texture, and local thermal-response cues; the Selective Boundary-Guided Aggregation module (SBGA) strengthens cross-level detail propagation in the neck; and the Multi-scale Guided Feature Recalibration module (MGFR) recalibrates the P3, P4, and P5 features before the Detect head. On RGBTDronePerson, MDCFT-HSM achieves 49.53% mAP@0.5 and 18.97% mAP@0.5:0.95 with 7.38 M parameters, 44.2 GFLOPs, and an inference speed of 60.0 FPS. On DroneVehicle, it achieves 83.60% mAP@0.5 and 63.28% mAP@0.5:0.95. Controlled robustness tests show limited tolerance to auxiliary RGB absence and mild cross-modal spatial misalignment, whereas severe IR degradation, IR absence, and larger spatial offsets cause substantial performance loss. These results demonstrate a competitive accuracy–complexity trade-off under a fixed dataset-level protected-branch configuration. The method does not provide online sample-level reliability adaptation or geometric registration.