论文标题
空中图像中的对象检测：什么提高了准确性？
Object Detection in Aerial Images: What Improves the Accuracy?
论文作者
论文摘要
对象检测是一个具有挑战性且流行的计算机视觉问题。由于规模和观点的显着差异，各种对象类别的规模和观点的显着差异，问题在空中图像中更具挑战性。最近，已经为空中图像中的对象检测问题积极探索了基于学习的对象检测方法。在这项工作中，我们研究了更快的R-CNN对空中对象检测的影响，并探索了许多策略以改善其空中图像的性能。我们对具有挑战性的ISAID数据集进行了广泛的实验。最终的适应性速度R-CNN获得了在ISAID验证集对其香草基线的显着地图增益4.96％，这表明了这项工作中研究的不同策略的影响。
Object detection is a challenging and popular computer vision problem. The problem is even more challenging in aerial images due to significant variation in scale and viewpoint in a diverse set of object categories. Recently, deep learning-based object detection approaches have been actively explored for the problem of object detection in aerial images. In this work, we investigate the impact of Faster R-CNN for aerial object detection and explore numerous strategies to improve its performance for aerial images. We conduct extensive experiments on the challenging iSAID dataset. The resulting adapted Faster R-CNN obtains a significant mAP gain of 4.96% over its vanilla baseline counterpart on the iSAID validation set, demonstrating the impact of different strategies investigated in this work.
