论文标题
调查航空图像中对象检测中类不平衡和规模变化的挑战
Investigating the Challenges of Class Imbalance and Scale Variation in Object Detection in Aerial Images
论文作者
论文摘要
尽管对象检测是计算机视觉中的一个常见问题，但处理空中卫星图像时，它更具挑战性。对象尺度和方向的多样性可以使它们难以识别。此外，可以有大量密集的小物体（例如汽车）。在这个项目中，我们对更快的RCNN体系结构提出了一些更改。首先，我们尝试使用不同的骨干来提取更好的功能。我们还为区域建议修改数据增强和生成的锚固尺寸，以便更好地处理小对象。最后，我们研究了不同损失函数的影响。我们提出的设计可在基线上获得4.7地图的改进，该基线使用RESNET-101 FPN主链使用香草的R-CNN加快了R-CNN。
While object detection is a common problem in computer vision, it is even more challenging when dealing with aerial satellite images. The variety in object scales and orientations can make them difficult to identify. In addition, there can be large amounts of densely packed small objects such as cars. In this project, we propose a few changes to the Faster-RCNN architecture. First, we experiment with different backbones to extract better features. We also modify the data augmentations and generated anchor sizes for region proposals in order to better handle small objects. Finally, we investigate the effects of different loss functions. Our proposed design achieves an improvement of 4.7 mAP over the baseline which used a vanilla Faster R-CNN with a ResNet-101 FPN backbone.
