Weather image recognition method based on lightweight convolutional neural network

Abstract

The invention discloses a weather phenomenon identification method based on a lightweight convolutional neural network, and belongs to the technical field of image identification. The method comprisesthe following steps: constructing a lightweight weather identification network; training a weather recognition network model; obtaining a weather picture to be identified and carrying out standardization processing; and inputting the processed data into the trained weather identification network and outputting the category of the data. The method makes full use of the advantages of the convolutional neural network in the field of large-scale image recognition, combines the ideas of deep separable convolution, attention mechanism, residual connection, transfer learning and the like, effectively reduces the calculation complexity of the model under the condition of not reducing the recognition precision, and provides possibility for the deployment of the model on small equipment.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a weather image recognition method based on a lightweight convolutional neural network. Background technique [0002] At present, in the field of meteorology, the identification of weather phenomena mainly relies on some hardware methods, such as weather radar and weather sensors. However, the cost of using hardware devices to identify weather phenomena is relatively high, and there are difficulties in maintenance, so it is difficult to deploy devices intensively to identify weather phenomena in a more refined manner. [0003] In recent years, with the growth of data volume and computing power, Convolution Neural Networks (CNNs) have become ubiquitous in various image tasks due to their outstanding performance. The three basic image tasks of image recognition, target detection, and image segmentation have made far more progress than before due to the addition of convol...

AI Technical Summary

Problems solved by technology

The early LeNet5 network used for handwritten digit recognition has only 60,000 parameters, but the current mainstream model has tens of millions or even hundreds of millions of parameters, which is difficult to deploy in some small devices

In addition, models with a large number of parameters are also prone to overfitting due to insufficient data volume, making it difficult to train

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