Research On Semantic Segmentation Algorithm Of Deep Convolutional Neural Network Based On Mixed Task Cascade

The rise of deep learning has caused a research boom in the field of artificial intelligence,and deep convolutional neural networks are a very reliable method in the field of computer vision.With the significant increase in image data and the increasing popularity of smart devices,it is necessary to quickly and accurately understand the content of the image and automatically identify and segment the target object in the image.At present,there are many excellent research results on image segmentation algorithms at home and abroad.When it is applied to the actual operation process,it is found that there are still many problems.For example,it is difficult to obtain satisfactory segmentation accuracy for the target object under interference such as partial object overlap and occlusion,background clutter,and light intensity.This paper conducts in-depth research and analysis on existing semantic segmentation algorithms,analyzes and improves on existing algorithms,and designs many adaptive pipelines and modules.The specific work of this paper is as follows:First of all,in order to better extract target image features at different scales,this paper changes the original residual structure and designs a parallel convolution module,and proposes a feature extraction network based on an improved residual network to expand the receptive field of the network.Ways to obtain more semantic information,and make sufficient preparations for the following embedded balanced hybrid cascaded semantic segmentation algorithm to reduce operational parameters.Secondly,this paper proposes a hybrid cascaded semantic segmentation algorithm with embedded balance improved residual network,which solves the problem of excessive blurring of segmentation edge lines when multiple targets overlap.When acquiring features,a network structure of deformable convolution and improved parallel residuals is used to obtain more spatial information;in feature fusion,it is found that the key to successful semantic segmentation fusion is to make full use of bounding box detection results and masks(Mask)predicts the associated information between the segmentation.Therefore,in order to further process the bounding box information and improve the information flow,this paper uses a cascaded pipeline to merge the Mask RCNN and Cascade RCNN network structures,and at each stage,through the mask prediction and boundary Box regression parallel fusion to obtain the mutual information relationship between them,so as to further improve the information flow in RCNN.In addition,Io U balanced sampling,balanced feature pyramid,and balanced L1 loss function are also added to the network model,which are used to reduce the imbalances in sample collection,feature extraction,and target detection,respectively.Finally,this paper proposes an optimized network that can obtain high-resolution image semantic segmentation results.In essence,it introduces the second image semantic segmentation algorithm studied in this paper,which solves the multiple deformation and sampling processes in the traditional full convolution model.This leads to problems such as low image resolution and unrecognizable objects.The idea of separation-transformation-merging is introduced to optimize the network,and the feature extraction network in the hybrid cascade structure of the residual network is improved by replacing the embedded balance with the HRNet network with the added cross unit.In addition,in the feature extraction module,a hybrid extension unit for processing multi-scale image objects is proposed.This module includes an alternative communication strategy that can perform multi-scale fusion,enhances the information sharing between the fuzzy boundary information of the picture,and greatly improves the model.Accuracy of segmentation.