Performance Optimization Of The Semantic Segmentation Framework Deeplab

In the real world,image data contains massive information of real objects,which is a description or objective mapping of objective objects.For the computer to better understand the real world and develop a variety of computer technologies,the semantic segmentation method based on deep learning can better extract the high-level semantic information of images and better deal with complex scenes.Therefore,it has attracted the attention of many researchers.The feature extraction part of the deep learning semantic segmentation algorithm is very important.The deeper the model,the stronger the feature extraction capability,but the more abstract the resulting features.Based on the Deeplab semantic segmentation framework,an optimization method is proposed,and the effectiveness of the optimization effect is demonstrated by comparing experimental results.Firstly,the difficult problems in the semantic segmentation task are analyzed.For these difficulties,the model structure of Deeplab V3 is optimized.The optimization method is divided into three parts.The first part of the optimization method is the structural optimization of the basic feature extraction network,and more spatial location information is captured in the shallow stage of the neural network.The second part of the optimization method is to distinguish the importance of the different channels of the deep feature map extracted by the deep neural network,and recalculate the importance of the different channel features.The third part of the optimization method is to combine the shallow feature map with the deep feature map,so that more spatial information can be obtained.Secondly,in order to better extract features,an auxiliary loss is added to the loss function design,so that the basic feature extraction network is more spatial information.Finally,for better training models,data enhancement is carried out for the characteristics of data and multi-scale problems faced by semantic segmentation tasks.Data enhancement methods include random flipping,random scaling and cropping,random addition of Gaussian noise,increased data diversity and increased multi-scale of training data.The comparison between the experimental results proves that the performance of the optimized Deeplab semantic segmentation model is improved.In order to better display the results,the Py Qt5 graphical interface framework is used to build semantic segmentation interfaces.