论文标题
进行渐进域的适应性，并在卫星图像中与对象检测进行对比学习
Progressive Domain Adaptation with Contrastive Learning for Object Detection in the Satellite Imagery
论文作者
论文摘要
用于卫星和无人机图像的最新对象检测方法在很大程度上无法识别小对象。原因之一是由于捕获的陆地区域以及采集条件的高变异性，架空图像中内容的高度差异。另一个原因是，航空影像中对象的数量和大小与消费者数据有很大不同。在这项工作中，我们提出了一条小型对象检测管道，该管道通过空间金字塔池，跨阶段部分网络，基于热图的区域提案网络以及对象定位和识别通过新的图像难度分数来改善对象的定位和识别，从而改善了特征提取过程，从而改善了基于整体焦点损失衡量标准的图像难度。接下来，我们提出具有渐进域适应性的新型对比学习，以使用本地和全球组件在空中数据集中产生域不变特征。我们表明，我们可以减轻以前看不见的数据集中对象识别的降解。我们使用对象检测任务在高度不平衡的卫星数据集中使用对象检测任务创建有史以来的第一个域适应基准，该数据集具有重要的域间隙和主导的小对象。所提出的方法导致地图性能度量比最佳最新最新性提高了7.4％。
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.
