Design And Optimization Of Skeleton Extraction Algorithm Based On Convolutional Neural Network

With the increasing of image data,the input of massive visual information puts forward higher requirements for the processing algorithm.As a compact representation of image,skeleton can display the shape of image foreground concisely,which plays an important role in the field of focusing on object shape and requiring small amount of data.Skeleton extraction technology has become a research hotspot.With the development of deep learning,the skeleton extraction algorithm based on convolutional neural network further improves the accuracy of the algorithm.However,with the deepening of the network,the running time and memory consumption of the algorithm increase rapidly.In order to balance the training consumption,it is necessary to design optimization algorithm on the network with limited depth.In view of the low accuracy of the existing algorithm,this paper studies and analyzes the skeleton extraction algorithm based on convolution neural network,and proposes the corresponding improved optimization algorithm.According to the side output residual network which can improve the learning ability and the skeleton scale which can reduce the supervision error,this paper proposes a new end-toend skeleton extraction algorithm,Fusing Scale-associated Side Outputs Residual Network(FSRN).This algorithm takes vgg-16 network as the basic framework,and modifies the network with skeleton scale information.Then from the decoding point of view,through the operations of dimensionality reduction,up sampling,summing and so on,the features are fused step by step.The experimental results show the feasibility of the algorithm.In order to improve the accuracy of the algorithm,this paper proposes a multi-path and multi-supervision skeleton extraction improvement scheme.By stacking FSRN units to form a ladder structure,the accuracy of the algorithm is improved,and the supervision layer is added to accelerate the convergence of the algorithm.In this paper,several groups of experiments are designed to compare and analyze the influence of different supervision number,different fusion path number and different fusion direction on the algorithm performance,and select the FSRN architecture with the best comprehensive performance.In order to further improve the performance of the algorithm,this paper combines the adjacent features of the same stage of the network from the coding point of view to enhance the performance of the features and eliminate the redundant shallow features to improve the efficiency of the algorithm.Through the comparison of experiments,the effectiveness of various optimization schemes is verified.The experimental results show that the proposed three-way three-layer FSRN with adjacent features fusion can effectively extract the skeleton of the object in image,and has higher recognition accuracy than the existing algorithms such as side-output residual network,fusing scale-associated deep side outputs network and two level hierarchical feature integration network,and has certain advantages in convergence speed,resource consumption and other evaluation indexes.