Application Of Hierarchical Computation Perceptual Simulation In Target Recognition Technology

With the deep understanding of human visual information processing mechanism,simulating the brain vision system to enhance the environmental perception and cognitive ability of computer vision has become one of the research hotspots in the field of computer vision.The main idea is to visual information in the visual cortex of the brain Level processing to simulate the target recognition.In recent years,the development of in-depth learning has made it possible for the machine to have a hierarchical visual feature perception.The feature extracted by depth learning is a hierarchical,highly abstract feature that can be solved in many areas such as target recognition.Better results.In this paper,a convolutional neural network with sparse connection and self-learning mechanism is used as a framework to integrate hierarchical and bionic ideas,and simulate the hierarchical information processing mechanism of human brain to realize the accurate identification of significant targets.Firstly,a multichannel Gabor filter bank was constructed to simulate the retinal ganglion cell receptive field visual stimulus response,replacing the first convolution layer of the traditional convolution neural network.On this basis,the "lateral inhibition" mechanism was introduced into the nerve cells,The local response value normalization layer was used to screen the response of neurons.In order to simulate the sparse activation of biological nerve cell layer,the modified nonlinear unit was used as the activation function,and the spatial pyramid random pool layer adaptive Finally,the model simulates the stochastic electrochemical process of the local electric current in the biological nerve fiber membrane,and uses the stochastic DropConnect method to solve the common over-fitting problem of the neural network computation..By modeling the functions of V1,V2,V4 and IT in the abdominal pathway,the proposed model constructs a hierarchical computing perceptual model with hierarchical computing perceptual invariance and learning and recognition ability.The model is applied to different scale data sets for training and verification.Under the same recognition condition,the model has better recognition accuracy and recognition stability than other target recognition models.