Constrained-SIoU: A Metric for Horizontal Candidates in Multi-Oriented Object Detection

Abstract

Intersection over union (IoU) has been widely adopted to evaluate and select candidate regions in multi-oriented object detection. Intuitively, overlaps between candidates and multi-oriented ground-truth boxes make more sense when assessing the quality of horizontal candidates. However, the horizontal minimum bounding box (HMBB) of the ground-truth box is generally used for the IoU calculation in practice, bringing about biased results. In this article, we propose a novel Splicing Intersection over Union (SIoU) to provide a more preferable metric for horizontal candidate selection when detecting multi-oriented objects. By computing the intersection between the candidate region and the ground-truth box rather than its HMBB, SIoU provides a better description of how much object information a candidate contains. Furthermore, we introduce two variants of constraints for the center of each candidate to ensure its location focusing on the objects. Candidates whose centers deviate too far from the objects will be penalized. We integrate the constraint with SIoU, denoted as constrained-SIoU, to select horizontal candidates more efficiently. Comparative experiments on two datasets of aerial images, DOTA and HRSC2016, demonstrate the effectiveness of the proposed method.

Keywords

• Aerial images
• label assignment
• multi-oriented object detection
• remote sensing