  Traditionally, an object detector is applied to every part of the scene of
interest, and its accuracy and computational cost increases with higher
resolution images. However, in some application domains such as remote sensing,
purchasing high spatial resolution images is expensive. To reduce the large
computational and monetary cost associated with using high spatial resolution
images, we propose a reinforcement learning agent that adaptively selects the
spatial resolution of each image that is provided to the detector. In
particular, we train the agent in a dual reward setting to choose low spatial
resolution images to be run through a coarse level detector when the image is
dominated by large objects, and high spatial resolution images to be run
through a fine level detector when it is dominated by small objects. This
reduces the dependency on high spatial resolution images for building a robust
detector and increases run-time efficiency. We perform experiments on the xView
dataset, consisting of large images, where we increase run-time efficiency by
50% and use high resolution images only 30% of the time while maintaining
similar accuracy as a detector that uses only high resolution images.
