Detecting Arbitrary-oriented Objects in Remote Sensing Imagery with Segmentation-Aware Mask

Arbitrary-Oriented object detection in remote sensing images is a hot topic in recent years. Currently, most arbitrary-oriented object detectors adopt the oriented bounding box (OBB) to represent targets in remote sensing imagery. However, OBB representation suffers from suboptimal regression problems caused by the ambiguity of the angle definition. In this paper, we propose a novel framework to Learning Segmentation-aware Mask for arbitrary-oriented object Detection (LSM-Det) in remote sensing imagery. LSM-Det predicts the mask of the object, and then converts the mask prediction into a minimum external OBB to achieve arbitrary-oriented object detection. Moreover, we designed a segmentation-aware branch to select high-quality predictions via the output matching score. Our method achieves superior performance on multiple remote sensing datasets. Code and models are available to facilitate related research.