Pollen image detection method and system based on feature fusion

Abstract

The invention provides a pollen image detection method based on feature fusion, and the method comprises the steps: inputting a preprocessed pollen image into a convolutional neural network, and obtaining shallow features; generating a spatial attention weighted feature map and a spatial attention weight matrix through a spatial attention module based on the shallow features; generating a deep feature map from the spatial attention weighted feature map through convolution and down-sampling, and generating a channel attention weighted feature map from the deep feature map through a channel attention module; inputting the space attention weight matrix and the channel attention weighted feature map into a cross-connection attention mechanism to obtain a feature map after feature fusion; and inputting the feature image after feature fusion into a prediction module to obtain a detection result of pollen information in the pollen image. The pollen detail information in the superficial layer features is weighted and fused to the deep layer features through the cross-connection attention mechanism, feature fusion is performed after the deep layer features are optimized, and more pollen details of the pollen image can be recovered.

Description

technical field [0001] The present invention relates to the technical field of image recognition, and more specifically, to a pollen image detection method and system based on feature fusion. Background technique [0002] With the continuous development of urbanization, pollen allergy has gradually become a major public health problem. The traditional artificial pollen monitoring method can no longer meet the current demand for pollen forecasting due to the disadvantages of long cycle and high cost. Therefore, the development of an automatic pollen identification system is of great significance for the normal life of pollen allergy patients. [0003] Pollen particle detection is the core task of the pollen automatic identification system, the purpose is to automatically detect the position of pollen from the picture and determine the category of pollen. The determination of the pollen category requires the support of relatively rich detailed information, mainly including t...

AI Technical Summary

Problems solved by technology

However, the downsampling ratio of deep features is high, which will lead to serious loss of pollen image details, and the shallow features contain more noise. Therefore, it is difficult to restore fine pollen details by direct summing, which will lead to pollen categories. The determination is not accurate, which affects the accuracy of the pollen detection model

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