LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images. (arXiv:2209.07709v1 [cs.CV])

A few lightweight convolutional neural network (CNN) models have been
recently designed for remote sensing object detection (RSOD). However, most of
them simply replace vanilla convolutions with stacked separable convolutions,
which may not be efficient due to a lot of precision losses and may not be able
to detect oriented bounding boxes (OBB). Also, the existing OBB detection
methods are difficult to constrain the shape of objects predicted by CNNs
accurately. In this paper, we propose an effective lightweight oriented …

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