State-of-the-art object detection methods applied to satellite and drone imagery largely fail to identify small and dense objects. One reason is the high variability of content in the overhead imagery due to the terrestrial region captured and the high variability of acquisition conditions. Another reason is that the number and size of objects in aerial imagery are very different than in the consumer data. In this work, we propose a small object detection pipeline that improves the feature extraction process by spatial pyramid pooling, cross-stage partial networks, heatmap-based region proposal network, and object localization and identification through a novel image difficulty score that adapts the overall focal loss measure based on the image difficulty. Next, we propose novel contrastive learning with progressive domain adaptation to produce domain-invariant features across aerial datasets using local and global components. We show we can alleviate the degradation of object identification in previously unseen datasets. We create a first-ever domain adaptation benchmark using contrastive learning for the object detection task in highly imbalanced satellite datasets with significant domain gaps and dominant small objects. The proposed method results in a 7.4% increase in mAP performance measure over the best state-of-art.

翻译：应用于卫星和无人机图像的最先进目标检测方法在识别小型密集目标方面效果不佳。原因之一是由于所捕获的地面区域以及采集条件的高度变化，导致航拍图像内容呈现高度变异性。另一个原因是航拍图像中目标的数量和尺寸与消费者数据存在显著差异。本研究提出一种小型目标检测流水线，通过空间金字塔池化、跨阶段局部网络、基于热图的区域提议网络，以及基于图像难度的自适应聚焦损失度量的目标定位与识别方法，来改善特征提取过程。此外，我们提出了一种结合渐进式域自适应的新颖对比学习框架，利用局部和全局组件在航拍数据集间生成域不变特征。研究表明该方法能够缓解模型在未见数据集上目标识别性能退化的问题。我们首次基于对比学习建立了高度不平衡卫星数据集的目标检测域自适应基准，该数据集具有显著域差异且以小型目标为主。所提方法较现有最优方法在平均精度均值上提升了7.4%。