PENet: A Joint Panoptic Edge Detection Network

• URL: http://arxiv.org/abs/2303.08848v1
• Date: Wed, 15 Mar 2023 18:01:01 GMT
• Title: PENet: A Joint Panoptic Edge Detection Network
• Authors: Yang Zhou, Giuseppe Loianno
• Abstract summary: We propose PENet, a novel detection network that combines semantic edge detection and instance-level perception into a compact panoptic edge representation. We validate the proposed panoptic edge segmentation method and demonstrate its effectiveness on the real-world Cityscapes dataset.
• Score: 9.115443186505958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, compact and efficient scene understanding representations have gained popularity in increasing situational awareness and autonomy of robotic systems. In this work, we illustrate the concept of a panoptic edge segmentation and propose PENet, a novel detection network called that combines semantic edge detection and instance-level perception into a compact panoptic edge representation. This is obtained through a joint network by multi-task learning that concurrently predicts semantic edges, instance centers and offset flow map without bounding box predictions exploiting the cross-task correlations among the tasks. The proposed approach allows extending semantic edge detection to panoptic edge detection which encapsulates both category-aware and instance-aware segmentation. We validate the proposed panoptic edge segmentation method and demonstrate its effectiveness on the real-world Cityscapes dataset.

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