A feature image recognition method based on a multi-attention space pyramid

Abstract

The invention discloses a feature image recognition method based on a multi-attention space pyramid, and belongs to the technical field of network mode identification. The method is characterized in that a shallow network deepening method is combined; the feature extraction capability of the convolutional neural network is enhanced; More abundant feature representations are obtained by fusing multi-scale attention features through an attention module, and a spatial pyramid pooling operation is added at the end of each branch network to perform multi-scale feature extraction, so that feature maps of any size are converted into feature vectors of fixed sizes to be sent to a full connection layer. The network provided by the invention can input images with any size without zooming or cutting,better retains the feature information of the images, and has better robustness and accuracy in the aspects of image fine recognition and low-pixel image classification recognition in a complex environment.

Description

technical field [0001] The invention belongs to the technical field of network pattern recognition, a multi-attention space pyramid feature extraction network structure, combined with a method of deepening the shallow network, strengthens the feature extraction ability of the convolutional neural network, and is an improved deep convolutional network structure . Background technique [0002] With the continuous development of mobile Internet and pattern recognition technology, the transmission of information has become faster, and the rapid increase in the amount of information has made the communication medium gradually shift from text to pictures or videos. Compared with the time-consuming and labor-intensive traditional image processing, deep learning has become a research hotspot in the fields of image recognition and artificial intelligence with its powerful data processing capabilities and high accuracy. [0003] Among them, the convolutional neural network is a data-...

AI Technical Summary

Problems solved by technology

[0005] In order to improve the performance of the network, so that it has better robustness and higher recognition accuracy in complex environments, it solves the problem that the accuracy of image recognition with low pixels and in complex environments is not high, and the image It is difficult to extract the fine-grained features of the environment, and a feature image recognition method based on the multi-attention space pyramid is proposed to strengthen the feature extraction ability of the convolutional neural network and realize fine image recognition in complex environments.

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