MDCFT-HSM: Modality-Shift-Aware RGB-T Small-Object Detection for Low-Altitude UAV Remote Sensing

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.