LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images

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 (SConvs), which may not be efficient due to a lot of precision losses and may not be able to detect oriented bounding boxes (OBBs). Also, the existing OBB detection methods are difficult to constrain the shape of objects predicted by CNNs accurately. In this article, we propose an effective lightweight oriented object detector (LO-Det). Specifically, a channel separation-aggregation (CSA) structure is designed to simplify the complexity of SConvs, 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 data sets demonstrate that the proposed LO-Det can run very fast even on embedded devices with the competitive accuracy of detecting oriented objects.

Automated summary

In this article, the authors propose a lightweight oriented object detector (LO-Det) for remote sensing object detection. They design a channel separation-aggregation (CSA) structure and a dynamic receptive field (DRF) mechanism to optimize efficiency and accuracy. They also introduce a diagonal support constraint head (DSC-Head) component to accurately and stably constrain the shape of oriented bounding boxes (OBBs). Experimental results demonstrate that LO-Det achieves fast runtime and competitive accuracy on embedded devices.