@inproceedings{26a882cbb27c429889f898fcf8fea398,
title = "YOLO-based panoptic segmentation network",
abstract = "Autonomous vehicles need information about their surroundings to safely navigate them. For this, the task of Panoptic Segmentation is proposed as a method of fully parsing the scene by assigning each pixel a label and instance id. Given the constraints of autonomous driving, this process needs to be done in a fast manner. In this paper, we propose the first panoptic segmentation network based on the YOLOv3 real-time object detection network by adding a semantic and instance segmentation branches. YOLO-panoptic is able to do real-time inference and achieves a performance similar to the state of the art methods in some metrics.",
author = "Manuel Diaz-Zapata and {\"O}zg{\"u}r Erkent and Christian Laugier",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 45th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2021 ; Conference date: 12-07-2021 Through 16-07-2021",
year = "2021",
month = jul,
doi = "10.1109/COMPSAC51774.2021.00170",
language = "English",
series = "Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1230--1234",
editor = "Chan, \{W. K.\} and Bill Claycomb and Hiroki Takakura and Ji-Jiang Yang and Yuuichi Teranishi and Dave Towey and Sergio Segura and Hossain Shahriar and Sorel Reisman and Ahamed, \{Sheikh Iqbal\}",
booktitle = "Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021",
address = "United States",
}