A Multi-Level Approach to Waste Object Segmentation

• URL: http://arxiv.org/abs/2007.04259v1
• Date: Wed, 8 Jul 2020 16:49:25 GMT
• Title: A Multi-Level Approach to Waste Object Segmentation
• Authors: Tao Wang and Yuanzheng Cai and Lingyu Liang and Dongyi Ye
• Abstract summary: We address the problem of localizing waste objects from a color image and an optional depth image. Our method integrates the intensity and depth information at multiple levels of spatial granularity. We create a new RGBD waste object segmentation, MJU-Waste, that is made public to facilitate future research in this area.
• Score: 10.20384144853726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the problem of localizing waste objects from a color image and an optional depth image, which is a key perception component for robotic interaction with such objects. Specifically, our method integrates the intensity and depth information at multiple levels of spatial granularity. Firstly, a scene-level deep network produces an initial coarse segmentation, based on which we select a few potential object regions to zoom in and perform fine segmentation. The results of the above steps are further integrated into a densely connected conditional random field that learns to respect the appearance, depth, and spatial affinities with pixel-level accuracy. In addition, we create a new RGBD waste object segmentation dataset, MJU-Waste, that is made public to facilitate future research in this area. The efficacy of our method is validated on both MJU-Waste and the Trash Annotation in Context (TACO) dataset.

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