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A Multi-Level Approach to Waste Object Segmentation

Tao Wang, Yuanzheng Cai, Lingyu Liang, Dongyi Ye
2020 Sensors  
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
more » ... 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.
doi:10.3390/s20143816 pmid:32650515 fatcat:nzlfqkj3j5dqbhebwysqwi2d5i