Multi-target detection method based on improved VGG16 network

Abstract

The invention discloses a multi-target detection method based on an improved VGG16 network. The multi-target detection method comprises the steps of 1, sample image enhancement in which a histogram equalization and histogram matching method is selected, and the display effect of the image is changed by changing the gray histogram of the image; 2, feature extraction model construction in whicha feature extraction network model is established and composed of a truncated VGGNet-16 network and an enhanced network layer, feature maps of different scales corresponding to parameters are generated ineach layer, target object detection is carried out on the feature maps of the different scales at the same time, and the feature maps of the different scales are used for predicting target objects ofdifferent scales; 3, setting a feature extraction model correlation function; 4, positioning a target on the extracted feature map; and 5, target positioning and setting of a feature classification loss function. According to the method, the recognition efficiency can be improved while the recognition precision is improved, so that the problems of difficult detection and difficult classification are solved.

Description

technical field [0001] The invention relates to a multi-target detection method based on an improved VGG16 network. [0002] technical background [0003] In recent years, with the rapid development of computer science and technology, image processing and image target detection based on computer technology have also achieved unprecedented rapid development. Among them, deep learning extracts key target features by learning massive digital image features. It has surpassed humans in detection and brought one surprise after another to the industry. With the re-emergence of neural networks, the video image method based on convolutional neural networks has become the mainstream technology of image segmentation and recognition, using template matching, edge feature extraction, gradient histogram and other means to achieve accurate recognition of images. Although image feature recognition based on neural network can effectively identify features for targets in complex scenes, and i...

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Problems solved by technology

[0004] 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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