Improved MaskFormer visual target segmentation method for noisy interference images

Aiming at the problem that the image is defaced with more noise and complex features that make the target difficult to be segmented accurately, the traditional CNN target segmentation method is difficult to fully extract the detail information. Based on this, a segmentation network based on improved MaskFormer is proposed in this paper. The lightweight mask attention mechanism is used instead of the Transformer decoding attention mechanism, and the foreground region of each prediction mask is constrained by cross-attention, which reduces the interference of the smeared region and enhances the extraction of local features; GAMS, a multi-scale feature fusion module based on the gating mechanism, is used to carve out the semantic information of the image at different scales, which improves the feature discriminative ability of the model; the exchange of the self-attention and the lightweight mask attention order to reduce the network computation and improve the model training efficiency. The optimal values of evaluation indexes such as MIoU, ssMIoU and msMIoU are obtained in the segmentation experiments on ADE20K and COCO-Stuff-10k datasets after noise defacement treatment, and the improved MaskFormer segmentation performance is better compared with other networks.