Clothing Image Segmentation Method Based on Deeplabv3+Fused with Attention Mechanism
Aiming at the problems of rough edge segmentation and low segmentation accuracy caused by color,texture,back-ground and multi-object occlusion in clothing image segmentation,an image semantic segmentation method(FFDNet)based on Deeplabv3+with attention mechanism is proposed.Firstly,the backbone network of the model uses the ResNet101 network.The feature-enhanced attention module(FEAM)is added at the end of it.The feature map is weighted from the two dimensions of channel and spatial to mine and enhance the feature information and optimize the segmentation edge to improve network clarity.Secondly,a feature align module(FAM)is introduced as a novel upsampling method to address the problem of segmentation er-rors and low efficiency caused by misalignment between features during the fusion of different scale features,so as to to improve the accuracy and robustness of clothing image segmentation.Finally,the mean intersection over union of the proposed method reaches 55.2％and 79.4％on Deepfashion2 and PASCAL VOC2012,respectively.In terms of parameter size,the model only in-creases by 0.61MB compared to the original model on Deepfashion2.The segmentation performance of the FFDNet is superior to the existing state-of-the-art network models,which can effectively capture image local detail information and reduce pixel classifi-cation errors.
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