A pattern recognition method based on deep convolution neural network

Abstract

The invention discloses a pattern recognition method based on a deep convolution neural network, mainly a pattern recognition method based on a deep convolution neural network and introducing an attenuation mechanism and an image enhancement means. Take that sample data set first, the dataset images are annotated by relevant professionals, to gray-scale an image, This highlights the outline of themain objective, so that feature extraction is facilitate, Then the data set is expanded by random rotation and different angles, and the image data is enhanced and pre-processed. Finally, a depth convolution neural network is constructed, which can extract image features efficiently. The model is trained and tested by 50% cross-validation of the established data set, and the vision-assisted detection model is constructed. The invention has higher operation efficiency in sample identification, and the model parameter is reduced, which reduces the occupation of resources and the high demand forsoftware and hardware, and can be put into actual use better.

Description

technical field [0001] The invention belongs to the field of deep learning computer vision, and mainly relates to a pattern recognition method based on a deep convolutional neural network and introducing an attention mechanism and image enhancement means. Background technique [0002] "Feature extraction + classifier" is a classic framework in the field of pattern recognition, that is, to represent images by constructing features manually, and then send the image data at the feature level to the classifier to realize the classification and recognition of target images. Neurological research shows that the human brain does not extract features in the process of processing visual images, but transmits signals to a deep network composed of a large number of neurons and passes them layer by layer to finally obtain the implicit expression of the signal. Deep learning is to let the image propagate in the network and output an effective representation of the image by simulating the...

AI Technical Summary

Problems solved by technology

However, these existing methods still have some defects

First of all, when labeling and segmenting images, operators need to have professional knowledge in related fields and be able to make professional interpretations of labeling sample points or texture features,...

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