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.
翻译:物体探测是一个具有挑战性和广受欢迎的计算机视觉问题。由于各种物体类别在规模和观点上的巨大差异,航空图像中的问题甚至更加具有挑战性。最近,针对空中图像中的物体探测问题,积极探索了深层次的基于学习的物体探测方法。在这项工作中,我们调查了更快的R-CNN对空中物体探测的影响,并探索了许多战略来改进其空中图像的性能。我们在具有挑战性的iSAID数据集上进行了广泛的实验。因此,经过调整的快速R-CNN在ISAID的验证集上比其香草基线对应器获得4.96%的大型MAP收益,这表明了在这项工作中调查的不同战略的影响。