Centerpoints Are All You Need in Overhead Imagery
This work addresses the labeling cost issue for researchers and practitioners in remote sensing and computer vision, presenting an incremental improvement by simplifying labeling requirements.
The paper tackled the problem of expensive and time-consuming data labeling for object detection in overhead imagery by proposing novel network architectures that use centerpoints for labeling, achieving nearly equivalent performance to methods requiring more detailed labeling on three datasets.
Labeling data to use for training object detectors is expensive and time consuming. Publicly available overhead datasets for object detection are labeled with image-aligned bounding boxes, object-aligned bounding boxes, or object masks, but it is not clear whether such detailed labeling is necessary. To test the idea, we developed novel single- and two-stage network architectures that use centerpoints for labeling. In this paper we show that these architectures achieve nearly equivalent performance to approaches using more detailed labeling on three overhead object detection datasets.