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.

翻译：作为计算机视觉中最根本和最具挑战性的问题之一,物体探测试图定位物体实例并在自然图像中找到其类别。在评价物体探测算法中最重要的一步是计算预测的捆绑框和地面真理一之间的交叉连接(IoU) 。虽然这一程序对于平面图像来说定义和解决了,但对于探测球形图像来说并不容易。现有的方法要么根据偏向捆绑框表示来计算IoU,要么作出过近近近近,这样可以得出错误的结果。在本文中,我们首先发现球形矩是球形图像中对象的不带偏见的捆绑框,然后提出一种分析方法,用于在没有近似值的情况下计算IoU。根据不偏向的表示和计算,我们还为球形图像提供了一种无锚物体探测算法。关于两个球形物体探测数据集的实验表明,拟议方法的性能优于现有方法。