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 multioriented 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.

Safety Helmet‐Wearing Detection System for Manufacturing Workshop Based on Improved YOLOv7

“…In the improved model, we replace the loss function with the SIoU function. In the SIoU function, the angle between the regression vector and the expected regression vector is considered, which helps to improve the training speed of the model and the accuracy of inference [28]. The SIoU function is shown in…”