论文标题
在360°图像中进行对象检测的视野IOU
Field-of-View IoU for Object Detection in 360° Images
论文作者
论文摘要
在过去的几年中，360°摄像机越来越受欢迎。在本文中，我们提出了两种基本技术-360°图像中的对象检测，视野IOU（fov-iou）和360 augnational。尽管大多数专为透视图像设计的对象检测神经网络适用于等应角投影（ERP）格式的360°图像，但由于ERP图像中的失真，它们的性能会恶化。我们的方法可以很容易地与现有的透视对象检测器集成在一起，并显着提高了性能。 FOV-iou计算球形图像中两个视野边界框的相交 - 可用于训练，推理和评估，而360Augmentions是一种数据增强技术，特定于360°对象检测任务，随机旋转球形图像，该任务旋转并旋转偏置，并因此旋转偏置，因此由于跨度的旋转范围plysports plysports plysports plysports propports propportsfort。我们在具有不同类型的透视对象检测器的360室数据集上进行了广泛的实验，并显示了我们方法的一致有效性。
360° cameras have gained popularity over the last few years. In this paper, we propose two fundamental techniques -- Field-of-View IoU (FoV-IoU) and 360Augmentation for object detection in 360° images. Although most object detection neural networks designed for the perspective images are applicable to 360° images in equirectangular projection (ERP) format, their performance deteriorates owing to the distortion in ERP images. Our method can be readily integrated with existing perspective object detectors and significantly improves the performance. The FoV-IoU computes the intersection-over-union of two Field-of-View bounding boxes in a spherical image which could be used for training, inference, and evaluation while 360Augmentation is a data augmentation technique specific to 360° object detection task which randomly rotates a spherical image and solves the bias due to the sphere-to-plane projection. We conduct extensive experiments on the 360indoor dataset with different types of perspective object detectors and show the consistent effectiveness of our method.
