DroneFINE: Domain-Aware Parameter-Efficient Fine-Tuning of Vision-Language Detectors for Drone Images

Object detection for Unmanned Aerial Vehicles (UAVs) work-ing in open and dynamic environments is a highly challenging task.While Vision-Language Models (VLMs) have offered a powerful solutionfor universal object detection, adapting them to UAV scenarios remainsnon-trivial due to a substantial domain gap between VLM pre-trainingdata and aerial imagery. The prevailing Parameter-Efficient Fine-Tuning(PEFT) methods prove ineffective in bridging this gap, as VLMs’ “natural-scene, foreground-dominant” visual priors misalign with the “bird’s-eye-view, background-dominant, small-object” characteristics of UAV data.To address this issue, we propose DroneFINE, a novel PEFT paradigmcomprising two domain-aware complementary modules tailored for VLM-based drone image detectors. Specifically, a data-dependent, foreground-aware, and multi-path adaptation mechanism named HyperAdapter isdesigned, which overcomes the static structural constraints of PEFT. Inaddition, a background suppression algorithm named SemanticGate isdeveloped, which is a text-conditioned guidance strategy that employsbackground vocabulary to actively guide the model in suppressing re-sponses from irrelevant regions. Extensive experiments on VisDrone andUAVDT demonstrate that DroneFINE significantly outperforms existingPEFT methods and achieves performance comparable to full fine-tuningwhile substantially reducing the fine-tuned parameters.