  State-of-the-art detection systems are generally evaluated on their ability
to exhaustively retrieve objects densely distributed in the image, across a
wide variety of appearances and semantic categories. Orthogonal to this, many
real-life object detection applications, for example in remote sensing, instead
require dealing with large images that contain only a few small objects of a
single class, scattered heterogeneously across the space. In addition, they are
often subject to strict computational constraints, such as limited battery
capacity and computing power. To tackle these more practical scenarios, we
propose a novel flexible detection scheme that efficiently adapts to variable
object sizes and densities: We rely on a sequence of detection stages, each of
which has the ability to predict groups of objects as well as individuals.
Similar to a detection cascade, this multi-stage architecture spares
computational effort by discarding large irrelevant regions of the image early
during the detection process. The ability to group objects provides further
computational and memory savings, as it allows working with lower image
resolutions in early stages, where groups are more easily detected than
individuals, as they are more salient. We report experimental results on two
aerial image datasets, and show that the proposed method is as accurate yet
computationally more efficient than standard single-shot detectors,
consistently across three different backbone architectures.
