FashionSegNet: a model for high-precision semantic segmentation of clothing images

Clothing image segmentation is a method to predict the clothing category label of each pixel in the input image. We reduced the influence of the variability of image shots, the similarity of clothing categories, and the complexity of boundaries on the segmentation accuracy of clothing images by developing an advanced ResNet50-based semantic segmentation model in this study whose primary structure is the encoder-decoder. An improved spatial pyramid pooling module combined with a global feature extraction branch of a large convolution kernel is developed to achieve multi-scale feature fusion and improve the model's ability to identify clothing and its boundary features in different shots. Furthermore, to balance the clothing shape and category information in the model, a spatial and semantic information enhancement module is proposed, which can enhance the circulation of the information between different stages of the network through cross-stage connection technology. The model was finally trained and tested on the Deepfashion2 dataset. The comparison experiment demonstrates that the proposed model obtained the highest mIoU and Boundary IoU of 74.55% and 57.51%, respectively, compared with the DeepLabv3+, PSPNet, and other networks.