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YOLO-based Panoptic Segmentation Network

Manuel Alejandro Diaz Zapata 1 Özgür Erkent 1 Christian Laugier 1 
1 CHROMA - Robots coopératifs et adaptés à la présence humaine en environnements dynamiques
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
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.
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https://hal.inria.fr/hal-03283640
Contributor : MANUEL DIAZ ZAPATA Connect in order to contact the contributor
Submitted on : Monday, July 12, 2021 - 1:33:06 PM
Last modification on : Monday, May 16, 2022 - 4:46:03 PM
Long-term archiving on: : Wednesday, October 13, 2021 - 8:15:41 PM

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Manuel Alejandro Diaz Zapata, Özgür Erkent, Christian Laugier. YOLO-based Panoptic Segmentation Network. COMPSAC 2021 - Intelligent and Resilient Computing for a Collaborative World 45th Anniversary Conference, Jul 2021, Madrid, Spain. pp.1-5, ⟨10.1109/COMPSAC51774.2021.00170⟩. ⟨hal-03283640⟩

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