Ship type recognition method and system based on transfer learning

Abstract

The invention provides a ship type identification method and a ship type identification system based on migration learning. The method comprises the following steps: migrating a trained convolution neural network model; adjusting parameters and structure of the convolution neural network model in combination with a ship image to obtain a migration learning model; The ship image is grayed, normalized and annotated, and the data set is obtained. Training the transfer learning model with the data set according to a back propagation method; The image to be measured is input into the trained transfer learning model for type recognition. The invention adopts a transfer learning method, which can actively extract features of warship images without manual extraction of features, reduces workload and leakage possibility of personnel, avoids the problem of poor network classification result caused by de novo network design and lack of a large number of warship data sets, and improves accuracy ofclassification.

Description

technical field [0001] The present invention relates to the technical field of image recognition, in particular to a ship type recognition method and system based on transfer learning. Background technique [0002] For ship objects, as the main carrier of maritime transportation, it plays a very important role in military and civilian use. Due to the development of the economy, more and more ships are operating at sea, and the sea traffic is becoming more and more busy, and the accidents caused by it are becoming more and more common. , reducing the probability to a certain extent. In addition, in the military, if the type of distant ship (civilian ship or military ship) can be correctly identified, it also has very important military significance. [0003] With the rise of deep learning, it has been widely used in the field of computer vision due to its accurate results. For deep learning, it often requires a large number of data sets to support it, but labeling new data...

AI Technical Summary

Problems solved by technology

[0005] (1) Increased the complexity of user operations;

[0006] (2) Due to the manual features, some inadvertent features may be ignored, and these features may play a certain role in the identification and classification of ships;

[0007] (3) For the color feature, it may be affected by light reflection in the sea surface, etc., resulting in inconspicuous features and lowering its role;

[0008] (4) Due to (2) and (3), the classification accuracy will be reduced

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