用无标注无人机数据提升雪天车辆检测，性能提升超3.8%。
Enhancing Vehicle Detection under Adverse Weather Conditions with Contrastive Learning
- 通过对比学习在无标注数据上预训练特征提取器
- 在NVD数据集上mAP50提升3.8%至9.5%
- 适合资源有限的遥感车辆检测场景
除遥感中常见的小目标、稀疏目标和计算成本限制外，北欧地区无人机图像中的车辆检测还面临强能见度挑战及由不同积雪覆盖程度引起的领域偏移。尽管标注数据成本高，但仅需飞行无人机即可低成本获取未标注数据。本文提出一种sideload-CL-adaptation框架，利用未标注数据提升轻量级模型的车辆检测性能。具体而言，在预训练阶段，基于未标注数据通过对比学习训练一个基于CNN的特征提取器；在微调阶段，将该提取器侧加载至冻结的YOLO11n主干网络。为寻找稳健的sideload-CL适应策略，我们系统比较了多种融合方法与粒度。所提模型在NVD数据集上以mAP50衡量的检测性能提升达3.8%至9.5%。
原文摘要 · Abstract (English)
Aside from common challenges in remote sensing like small, sparse targets and computation cost limitations, detecting vehicles from UAV images in the Nordic regions faces strong visibility challenges and domain shifts caused by diverse levels of snow coverage. Although annotated data are expensive, unannotated data is cheaper to obtain by simply flying the drones. In this work, we proposed a sideload-CL-adaptation framework that enables the use of unannotated data to improve vehicle detection using lightweight models. Specifically, we propose to train a CNN-based representation extractor through contrastive learning on the unannotated data in the pretraining stage, and then sideload it to a frozen YOLO11n backbone in the fine-tuning stage. To find a robust sideload-CL-adaptation, we conducted extensive experiments to compare various fusion methods and granularity. Our proposed sideload-CL-adaptation model improves the detection performance by 3.8% to 9.5% in terms of mAP50 on the NVD dataset.
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