A multi-target detection method based on improved vgg16 network

Abstract

A multi-target detection method based on the improved VGG16 network, comprising: step 1, sample image enhancement processing; selecting the method of histogram equalization and histogram matching, and changing the display effect of the image by changing the gray histogram of the image; Step 2, constructing a feature extraction model, constructing a feature extraction network model, consisting of a truncated VGGNet-16 network and an enhanced network layer, in each layer will generate feature maps of different scales corresponding to the parameters, then the detection of the target object is in Simultaneously on these feature maps of different scales, the feature maps of different scales are used to predict target objects of different scales; step 3, set the function of the feature extraction model; step 4, locate the target on the extracted feature map ; Step five, target positioning and feature classification loss function setting. The invention can speed up the recognition efficiency while improving the recognition precision, thereby solving the problems of difficult detection and classification.

Description

technical field [0001] The invention relates to a multi-target detection method based on an improved VGG16 network. Background technique [0002] In recent years, with the rapid development of computer science and technology, image processing, image target detection, etc. based on computer technology have also achieved unprecedented rapid development. It has surpassed humans in detection, bringing one surprise after another to the industry. With the re-emergence of neural network, the video image method based on convolutional neural network has become the mainstream technology of image segmentation and recognition. It adopts template matching, edge feature extraction, gradient histogram and other means to achieve accurate image recognition. Although image feature recognition based on neural network can perform effective feature recognition for targets in complex scenes, and its effect is much better than traditional methods, it also has shortcomings: (1) It is weak against ...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a multi-target detection method based on the improved VGG16 network for the problems of traditional detection methods such as cumbersome operation, low recognition accuracy, and slow recognition.

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