Improved Similar Target Detection In VGG Network Based On Visualization

Target detection is widely used in intelligent transportation,intelligent city,intelligent security and remote sensing.However,when the targets to be detected are concentrated in the same area,for example,when a large number of vehicles are detected on the congested road,the detection efficiency of the existing target detection algorithm is not good,prone to misidentification,missing identification and other phenomena,resulting in the unsatisfactory detection rate,often unable to accurately detect and count the number of targets to be detected.So this paper in the same area intensive appear similar goals,on the basis of the existing target detection technology,through the use of the network characteristics of hot tries to optimize VGG-16 convolution neural network for feature extraction,obtain dense appear similar goals within the same area of structure characteristics and spatial characteristics,effective detection of dense similar goals,Finally,the edge detection technology is used to obtain the detection target region to achieve the ideal effect of similar target detection.The main work is as follows:The target detection algorithm of traditional convolutional neural network is a typical black box problem.The network only has input and output in the training,and manual cannot intervene in the training process of the network.Therefore,to solve this problem,this paper applies the network characteristic thermogram in each step of the convolutional neural network training to guide the network training.Network characteristic thermal map can reflect the enrichment of network features in neural network training,and finally achieve target detection by extracting feature classification candidate areas.Experimental results show that compared with traditional neural networks,the convolutional neural network optimized by network characteristic thermal map has improved training efficiency and target detection rate in the field of dense similar target detection.In view of the feature extraction module network model of the single,such problems as inadequate target feature extraction,this paper improves a dense target recognition based on multi-scale convolution network architecture,through the establishment of multi-scale convolution networks with different convolution kernel feature extraction,and then extracted to multi-scale characteristics of the input to the optimized target identification in the network is used to identify the target,Finally,similar target detection is completed through the candidate area after classification optimization.Experiments show that the optimized method in this paper has further improved in the field of dense similar target detection.Compared with the traditional target detection method,the optimized method in this paper improves the accuracy by 2.44 percentage points,while the missed selection rate is reduced by 1.82 percentage points,and the accuracy is improved by 0.24 percentage points compared with the third chapter.At the same time,the missed separation rate decreased by 0.45 percentage points.