Research on Improved Image Segmentation Method Based on DeepLabV3+Model
In recent years,Computer Vision has developed rapidly,in which image segmentation also plays a decisive role in Computer Vision,and has been fully applied in urban modernization construction,intelligent driving,geographic survey,and so on.However,most segmentation methods only focus on the simple fusion of vertical deep features and shallow features of image features,while ignoring the horizontal remote relationship of image features in the same layer.To address this problem,based on the DeepLabV3+framework,the Swin-Transformer block is added,and its Self-Attention Mechanism feature is utilized for network feature extraction in order to improve the global and detailed optimization of image segmentation.Secondly,the up-sampling method in DeepLabV3+is improved,and CARAFE up-sampling module is utilized to replace the simple Bilinear Interpolation method.Experiments show that the improved model increases MIoU by 2%and ACC by 1%compared with the baseline model.
万方数据
维普
