Year
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(Preprint) Unbiased IoU for Spherical Image Object Detection
Qiang Zhao 赵强 ¹, Bin Chen ¹, Hang Xu 徐岗 ², Yike Ma 马宜科 ¹, Xiaodong Li ³, Bailan Feng ³, Chenggang Yan 颜成钢 ², Feng Dai 代锋 ¹
¹ Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
中国 北京 中国科学院计算技术研究所
² Hangzhou Dianzi University, Hangzhou, China
中国 杭州 杭州电子科技大学
³ Huawei Technologies Co., Ltd.
华为技术有限公司
arXiv, 2021-08-18
Abstract

As one of the most fundamental and challenging problems in computer vision, object detection tries to locate object instances and find their categories in natural images. The most important step in the evaluation of object detection algorithm is calculating the intersection-over-union (IoU) between the predicted bounding box and the ground truth one. Although this procedure is well-defined and solved for planar images, it is not easy for spherical image object detection.

Existing methods either compute the IoUs based on biased bounding box representations or make excessive approximations, thus would give incorrect results. In this paper, we first identify that spherical rectangles are unbiased bounding boxes for objects in spherical images, and then propose an analytical method for IoU calculation without any approximations. Based on the unbiased representation and calculation, we also present an anchor free object detection algorithm for spherical images. The experiments on two spherical object detection datasets show that the proposed method can achieve better performance than existing methods.
Unbiased IoU for Spherical Image Object Detection_1
Unbiased IoU for Spherical Image Object Detection_2
Unbiased IoU for Spherical Image Object Detection_3
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