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.
关键词
视觉传感图像/卷积神经网络/目标识别/纹理特征差异/Laplace损失函数/分块标记
Key words
visual sensing image/convolutional neural network/target identification/texture feature difference/Laplace loss function/block mark
维普
万方数据