Clothing Image Segmentation Network Based on Improved Deeplab v3+

Abstract

To solve the problems of rough clothing edge segmentation, unsatisfactory segmentation accuracy, and insufficient deep semantic feature extraction in clothing image segmentation, the Coordinate Attention(CA) mechanism and Semantic Feature Enhancement Module(SFEM) are embedded into the Deeplab v3+ network, whichfeatures good semantic segmentation performance, and a CA_SFEM_Deeplab v3+ network is proposed for clothing image segmentation in this study.To strengthen the learning of effective features in clothing images, the CA mechanism module is embedded into resnet101, which is the backbone network of the Deeplab v3+ network, and the feature map after convolution pooling is performed on a pyramid with holes is input into the SFEM for feature enhancement.Consequently, the segmentation accuracy improved.Experimental results show that the mean Intersection over Union(mIoU) and Mean Pixel Accuracy(MPA) of the CA_SFEM_Deeplabv3 + network are 0.557 and 0.671, respectively, in the DeepFashion2 dataset, which are 2.1% and 2.3% higher than those of the Deeplab v3 + network, respectively.Compared with the Deeplab v3+ network, the proposedCA_SFEM_Deeplab v3+offersa finer segmentation of the clothing contour and better segmentation performance.