基于双层解耦策略和注意力机制的遮挡目标分割

Occluded object segmentation is a difficult problem in instance segmentation, but it has great practical value in many industrial applications such as stacked parcel segmentation on logistics automatic sorting. In this paper, an occluded object segmentation method based on bilayer decoupling strategy and attention mechanism is proposed to improve the segmentation performance of occluded parcels. Firstly, the image features are extracted through a backbone network with a Feature Pyramid Network (FPN); Secondly, the bilayer decoupling head is used to predict whether the mass centers of instances are occluded, and different occlusion types of instances are predicted through different branches; Thirdly, attention refinement module is used to obtain predicted masks of non-occluded instances and generate an attention map by combining these masks; Finally, this attention map is used to help the prediction of occluded instances. A dataset is provided for occluded parcel segmentation. Our method is tested on this dataset. The experimental results show that the proposed network achieves 95,66% Average Precision(AP), 97.17% Recall, and 11.78% Miss Rate(MR^–2). It indicates that this method has better segmentation performance than other methods.