  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 object
detector (LO-Det). Specifically, a channel separation-aggregation (CSA)
structure is designed to simplify the complexity of stacked separable
convolutions, and a dynamic receptive field (DRF) mechanism is developed to
maintain high accuracy by customizing the convolution kernel and its perception
range dynamically when reducing the network complexity. The CSA-DRF component
optimizes efficiency while maintaining high accuracy. Then, a diagonal support
constraint head (DSC-Head) component is designed to detect OBBs and constrain
their shapes more accurately and stably. Extensive experiments on public
datasets demonstrate that the proposed LO-Det can run very fast even on
embedded devices with the competitive accuracy of detecting oriented objects.
