用大模型提升遥感目标检测在少标注下的精度与稳定性
LLM-Assisted Semantic Guidance for Sparsely Annotated Remote Sensing Object Detection
- 借助大模型语义理解能力生成高置信伪标签
- 在DOTA和HRSC2016上实现比现有方法更高的检测性能
- 适合遥感图像少样本检测任务的科研与工程应用
遥感目标检测中稀疏标注因密集目标分布和类别不平衡带来挑战。尽管现有密集伪标签方法在伪标签生成方面展现出潜力，但仍受限于选择歧义与置信度估计不一致问题。本文提出一种面向稀疏标注遥感目标检测的LLM辅助语义引导框架，利用大语言模型（LLM）的高级语义推理能力，提炼高置信伪标签。通过引入LLM生成的语义先验，我们设计了类感知的密集伪标签分配机制，自适应地为未标注及稀疏标注数据分配伪标签，确保在不同数据分布下的鲁棒监督。此外，提出自适应难负样本重加权模块，通过缓解混淆背景信息的影响来稳定监督学习分支。在DOTA和HRSC2016上的大量实验表明，该方法优于现有单阶段检测器框架，在稀疏标注条件下显著提升检测性能。
原文摘要 · Abstract (English)
Sparse annotation in remote sensing object detection poses significant challenges due to dense object distributions and category imbalances. Although existing Dense Pseudo-Label methods have demonstrated substantial potential in pseudo-labeling tasks, they remain constrained by selection ambiguities and inconsistencies in confidence estimation.In this paper, we introduce an LLM-assisted semantic guidance framework tailored for sparsely annotated remote sensing object detection, exploiting the advanced semantic reasoning capabilities of large language models (LLMs) to distill high-confidence pseudo-labels.By integrating LLM-generated semantic priors, we propose a Class-Aware Dense Pseudo-Label Assignment mechanism that adaptively assigns pseudo-labels for both unlabeled and sparsely labeled data, ensuring robust supervision across varying data distributions. Additionally, we develop an Adaptive Hard-Negative Reweighting Module to stabilize the supervised learning branch by mitigating the influence of confounding background information. Extensive experiments on DOTA and HRSC2016 demonstrate that the proposed method outperforms existing single-stage detector-based frameworks, significantly improving detection performance under sparse annotations.
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