Target Enhancement Recognition of Vision Sensing Image Based on Multi Dataset Deep Learning
The feature variation of visual sensing image objects is generally large,and the samples among categories are unbalanced.In or-der to improve the recognition effect of key objects in visual sensing images,a method of target enhancement recognition in visual sensing images based on multi dataset depth learning is proposed.Color feature is used to extract the feature quantity of object recognition,and the convolution neural network model with multiple feature parameters is constructed by calculating the texture feature difference between the object and the background area of the visual sensing image.Exponential Laplace loss function is used to reduce the variation range of intra class features in the model and adjust the distance between features of different centers.The whole process of target enhancement recogni-tion is completed by combining adaptive block marking.Taking the visual sensing images of flame and vehicle monitoring as the analysis object,the test experiment is designed.The results show that the visual sensing image enhancement effect of the proposed method is good,the abnormal behavior of fire and vehicle driving behavior in the visual sensing image can be effectively recognized,and the target recogni-tion time is less than 6.2 seconds,which proves that the proposed method has high practical application value.
visual sensing imageconvolutional neural networktarget identificationtexture feature differenceLaplace loss functionblock mark
万方数据
维普
