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

2026-06-30

AI Summary

Existing vision-language models exhibit limited performance in drone image object detection due to significant domain discrepancies, and conventional parameter-efficient fine-tuning (PEFT) approaches struggle to address the unique challenges of aerial imagery—namely, the bird’s-eye view, background-dominated scenes, and extremely small objects. To overcome these limitations, this work proposes DroneFINE, a novel domain-aware dual-module framework. It introduces a dynamic multi-path HyperAdapter for flexible feature adaptation and a text-guided SemanticGate to effectively suppress irrelevant background clutter. By transcending the static architectural constraints of traditional PEFT methods, DroneFINE achieves substantial performance gains on the VisDrone and UAVDT benchmarks, closely approaching the accuracy of full fine-tuning while requiring only a minimal number of trainable parameters.

Abstract

Object detection for Unmanned Aerial Vehicles (UAVs) working in open and dynamic environments is a highly challenging task. While Vision-Language Models (VLMs) have offered a powerful solution for universal object detection, adapting them to UAV scenarios remains non-trivial due to a substantial domain gap between VLM pre-training data 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 paradigm comprising 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 is designed, which overcomes the static structural constraints of PEFT. In addition, a background suppression algorithm named SemanticGate is developed. It is a text-conditioned guidance strategy that employs background vocabulary to actively guide the model in suppressing responses from irrelevant regions. Extensive experiments on VisDrone and UAVDT demonstrate that DroneFINE significantly outperforms existing PEFT methods and achieves performance comparable to full fine-tuning while substantially reducing the number of trainable parameters.

Problem

• Vision-Language Models
• Unmanned Aerial Vehicles
• Domain Gap
• Parameter-Efficient Fine-Tuning
• Object Detection

Innovation

• Parameter-Efficient Fine-Tuning
• Vision-Language Models
• Domain Adaptation
• Drone Object Detection
• Background Suppression