Training Real-Time Panoramic Object Detectors with Virtual Dataset
Qing-Yang Shen, Tian-Guo Huang, Peng-Xin Ding, Jia He
Abstract
With the rapid development of autonomous driving, real-time object detection on 360° images becomes more and more important. In this paper, we propose a panoramic virtual dataset for training object detectors on 360° images. The most important feature of our dataset includes (1) an auto-generated city scene is created for rendering 360° dataset. (2) annotation work for this dataset is automatic. In addition, we propose a modified YOLOv3 model called Pano-YOLO for real-time panoramic object detection. Compared with YOLOv3, mAP of Pano-YOLO drops 0.39%. While speed is 32.47% faster. Experiments are performed to show that models trained on our virtual dataset can be applied in real world. And Pano-YOLO is capable of real-time object detection task on high-resolution 360° panoramic images and videos.
BibTeX
@inproceedings{icassp2021_trainingrealtime,
title = {Training Real-Time Panoramic Object Detectors with Virtual Dataset},
author = {Qing-Yang Shen and Tian-Guo Huang and Peng-Xin Ding and Jia He},
booktitle = {ICASSP 2021},
year = {2021}
}