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Op-PSA: an Instance Segmentation Model for Occlusion of Garbage

EasyChair Preprint no. 10712

12 pagesDate: August 15, 2023


With the increasing emphasis on green development, garbage classification has become one of the important elements of green development. However, in scenarios where garbage stacking occurs, the task of segmenting highly overlapping objects is difficult because the bottom garbage is in an obscured state and its contours and obscured boundaries are usually difficult to distinguish. In this paper, we propose an Op-PSA model, which uses the HTC model as the baseline model and improves the modeling method of backbone network and model interest region using attention model and occlusion perception model. The Op-PSA model constructs the image as two overlapping layers and uses the two-layer structure to explicitly model the occluded and occluded objects, so that the boundaries of the occluded and occluded objects are naturally decoupled, and their interactions are considered in the mask regression. It is experimentally verified that the model can effectively detect the masked garbage and improve the detection accuracy of the masked garbage.

Keyphrases: Attention Model, Garbage detection, instance segmentation, Occlusion recognition

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Sheng Yu and Fei Ye},
  title = {Op-PSA: an Instance Segmentation Model for Occlusion of Garbage},
  howpublished = {EasyChair Preprint no. 10712},

  year = {EasyChair, 2023}}
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