  We introduced a high-resolution equirectangular panorama (360-degree, virtual
reality) dataset for object detection and propose a multi-projection variant of
YOLO detector. The main challenge with equirectangular panorama image are i)
the lack of annotated training data, ii) high-resolution imagery and iii)
severe geometric distortions of objects near the panorama projection poles. In
this work, we solve the challenges by i) using training examples available in
the "conventional datasets" (ImageNet and COCO), ii) employing only
low-resolution images that require only moderate GPU computing power and
memory, and iii) our multi-projection YOLO handles projection distortions by
making multiple stereographic sub-projections. In our experiments, YOLO
outperforms the other state-of-art detector, Faster RCNN and our
multi-projection YOLO achieves the best accuracy with low-resolution input.
