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Labeling data to use for training object detectors is expensive and timeconsuming. Publicly available overhead datasets for object detection arelabeled with image-aligned bounding boxes, object-aligned bounding boxes, orobject masks, but it is not clear whether such detailed labeling is necessary.To test the idea, we developed novel single- and two-stage networkarchitectures that use centerpoints for labeling. In this paper we show thatthese architectures achieve nearly equivalent performance to approaches usingmore detailed labeling on three overhead object detection datasets.

Centerpoints Are All You Need in Overhead Imagery