Research On Context Semantic Segmentation Algorithm Based On Deep Network

With the continuous development and maturity of deep learning methods,semantic segmentation based on deep convolutional networks has become an important technology for visual perception applications in the fields of autopilot,indoor navigation and remote sensing mapping.However,for weak or fuzzy targets,the semantic segmentation method still has the problem of low classification accuracy.In order to improve the accuracy of semantic segmentation,we have carried out the following two aspects of research workFirstly,inspired by the deformable convolution,the fixed sampling mode of ordinary convolution is discarded,and a variable direction convolution network is proposed to achieve more flexible variable-direction sampling.By dividing the neighborhood of each point on the feature map into five different directions,and extracting the multi-scale local features of the five regions,and simultaneously predicting the weighting factors of the five regional features,and then weighting factor is re-assigned by using the softmax soft constraint.So the weight of the salient features is magnified and the interference features are suppressed so that the semantic features become more salient.Although the variable convolution adds a small amount of computation,multi-scale local context features are incorporated for the model,our algorithm achieves higher precision than the original FCN(Fully Convolutional Networks)Secondly,a global context segmentation algorithm for residual coding and dynamic routing coding based on micro U-shaped networks is proposed.On the basis of the original U-type network,a simplified U-shaped network with fewer sampling layers is adopted,and the coarse-grained semantic features are extracted.However,two strategies,residual coding and dynamic routing coding are introduced to unsupervised clustering of the global context of the scene,and more representative semantics are obtained instead of the original semantics obtained through global pooling.Although the computational complexity is increased to some extent,our algorithm is more accurate than FCN.Although the amount of calculation is increased to some extent,our algorithm is more accurate than FCNWe have done a lot of experiments on Pascal VOC2012 and Pascal Context datasets respectively.The experiments show that our algorithm greatly improves the segmentation effect of objects.Compared with the existing classical algorithms,our algorithm achieves better results.