Study On Visual Cognition Learning Algorithm From Human-machine Semantic Consistency

In recent years,deep learning driven by big data has become the standard method for machine vision tasks such as face recognition,target detection and image segmentation,and it has enabled the machine to obtain visual perception ability close to human level.However,in practical application,the machine’s understanding of image semantics is far from reaching the depth of human cognition,and there is still a "semantic gap" in cognitive characteristics between machine vision and human vision.In order to further develop the cognitive model of machine vision,which approaches the semantic cognitive ability of human brain,and carry out experimental research based on actual scenes.In this paper,by introducing the technical perspective of human-computer semantic consistency cognition,and starting from the special visual processing mode of visual illusion cognition,the modeling ability of deep neural network to human visual illusion cognition is explored,and the application research for camouflage covert design is carried out.The main contents are as follows:1.Traditionally,the cognitive process of human visual illusion is regarded as an abnormal and strange visual processing mode,and it is of great theoretical significance for the development of machine vision technology to deeply explore its internal mechanism.Considering that the deep neural network has been able to realize the image recognition performance that is extremely high or even beyond the accuracy of human vision,this paper considers constructing a special experimental scene for in-depth research.First of all,in the virtual three-dimensional space,consider constructing the checkerboard shadow and plane figure of three-dimensional graphics and comparing two kinds of illusion scenes at the same time,and introduce the simulation generation strategy of two-dimensional illusion image data set in three-dimensional scenes to generate a batch of scene image data with illusion characteristics;Then,many semantic segmentation deep neural networks,such as UNet,Seg Net,PSPNet,Deepplabv3+,are used to carry out the experiment of visual illusion recognition and segmentation performance.The experimental results show that the segmentation performance evaluation indexes PA,MPA and MIOU of the above semantic segmentation model can all reach more than 0.95,which shows that the cognitive results of human visual illusion can also be well modeled and expressed by the deep neural network as a whole.2.The above research shows that deep neural network also has a good modeling ability for human visual illusion cognition,that is,machine vision and human visual perception can form a consistent visual recognition ability.Combined with practical application,optical illusion has great value in the field of military equipment camouflage covert design.Therefore,aiming at the problem of camouflage target concealment design in typical scenes,combined with evolutionary optimization strategy,a new camouflage concealment design method that imitates human visual cognition is proposed in this paper.On the one hand,the new method combines evolutionary computation strategy and image simulation means,and introduces image feature fusion degree calculation strategy based on VGG-16 model and search calculation strategy based on probability distribution sampling into particle swarm optimization algorithm from the perspective of imitating human normal visual cognition.On the other hand,from the perspective of imitating human visual illusion cognition,camouflage target concealment calculation covered by optical illusion strips is introduced.In the research,Deep Lab-Res Net semantic segmentation model is adopted,and the recognition rate of camouflage target segmentation is tested after model training to evaluate the performance of the new method.The simulation results show that the camouflage target design results with optical illusion cognition can achieve a higher degree of target concealment.To sum up,similar to other visual recognition tasks,machine vision can also accurately model the cognitive ability of human visual illusion,which can provide significant inspiration for developing stronger machine vision algorithms.Further research also shows that the camouflage design method of increasing the perception of human visual illusion can obtain better camouflage target concealment ability,which has clear practical value.