Spatially bound categorical attention network for semantic segmentation

Abstract

Semantic segmentation is a key step of image prehension. Single use of convolutional networks for semantic segmentation makes it difficult to distinguish the same class of objects with large contour deviations, while the higherlevel features will lose some the detailed information. Currently, Networks such as ACFNet and DANet have introduced attention mechanism to improve scene classification by obtaining rich contextual information through self-controlled system, but they do not combine both global scope and class feature relationships in local space to further advance intraclass consistency and inter-class divisibility of features. In terms of this problem, semantic segmentation network of categorical attention with spatial constraints has been proposed, which contains two submodules, one using the category spatial distribution to introduce local spatial location information of features, and the other using the global category average strength to introduce global strength information of category features. By selecting a kind of appropriate backbone network, this network model obtains the feature map from the backbone network and stacks the features into the original features after two submodules of global category strength and category local space processing, finally, performing classification processing by the classification layer and up-samples to the input image size to complete the pixel-level label prediction. The experiment result demonstrates that this proposed segmentation network has higher accuracy than existing segmentation networks.