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.

翻译：除了遥感领域中常见的小目标、稀疏目标以及计算成本限制等挑战外，在斯堪的纳维亚地区，从无人机图像中检测车辆还面临着严重的能见度挑战以及由不同程度积雪覆盖引起的域偏移问题。尽管标注数据获取成本高昂，但通过简单的无人机飞行即可较为廉价地获取未标注数据。在本工作中，我们提出了一种旁路加载-对比学习自适应框架，该框架能够利用未标注数据，通过轻量级模型提升车辆检测性能。具体而言，我们提出在预训练阶段通过对比学习在未标注数据上训练一个基于CNN的特征提取器，然后在微调阶段将其旁路加载到一个冻结的YOLO11n骨干网络中。为了找到一种鲁棒的旁路加载-对比学习自适应方案，我们进行了大量实验，比较了多种融合方法与粒度级别。我们提出的旁路加载-对比学习自适应模型在NVD数据集上，以mAP50为指标，将检测性能提升了3.8%至9.5%。