Pyramid network Chinese herbal medicine identification method based on attention mechanism

Abstract

The invention discloses a pyramid network Chinese herbal medicine identification method based on an attention mechanism, which comprises the following steps: 1) constructing a Chinese herbal medicinedata set, and making a Chinese herbal medicine training set and a Chinese herbal medicine test set; 2) constructing a feature fusion structure block based on a channel attention mechanism, and introducing a competition attention module; 3) adding a spatial attention mechanism to the feature fusion structure block of the pyramid network, adjusting the two information flows by using a spatial collaborative attention module, and fusing the two adjusted information flows as output; 4) constructing a pyramid network based on an attention mechanism, and training by using a Chinese herbal medicine training set; and 5) transmitting the pictures in the Chinese herbal medicine test set to the trained network model to identify the Chinese herbal medicine types corresponding to the pictures, thereby improving the Chinese herbal medicine identification accuracy and performance, assisting related industrial personnel to identify the Chinese herbal medicines, and facilitating non-professionals to identify the Chinese herbal medicines.

Description

technical field [0001] The invention relates to the application technical field of image recognition in the computer field and Chinese herbal medicine recognition in the field of traditional Chinese medicine, in particular to a Chinese herbal medicine recognition method based on a pyramid network of an attention mechanism. Background technique [0002] Image classification and recognition technology is based on digital images, extracting features from images to identify and judge the category of images. Deep convolutional neural networks have achieved great success in the field of image processing [C.Szegedy, W.Liu, Y.Jia, P.Sermanet, S.Reed, D.Anguelov, D.Erhan, V.Vanhoucke, and A. Rabinovich.Going deeper with convolutions.CVPR,2015.], it can automatically model and extract features from complex image data sets, so it has been widely used in target detection [R.Girshick.Fast r-cnn.Proceedings of the IEEE international conference on computer vision, 2015] and image recognit...

AI Technical Summary

Problems solved by technology

The former often requires specific equipment to extract the characteristics of Chinese herbal medicines, which is not easy to promote and use; while the existing methods of the latter still use traditional image features for recognition. These relatively single traditional features have limited expressive ability, thus limiting the recognition effect

Moreover, the feature extraction and learning process of the above methods are separated, which brings the discontinuity of parameter optimization

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