The application discloses a
sonar image classification method based on neural architecture search, first, the
sonar images obtained by the forward-looking
sonar, three-dimensional
imaging sonar and side-scan sonar are pretreated and screened; the sonar image data are divided into three subsets of a
training set, a validation set and a
test set; based on the characteristics of the sonar image, the search space and the search strategy are improved, the
convolution block SIC Block with multi-scale
feature extraction capability is proposed, and the DARTS-SIC
algorithm is obtained; the subsets are input into the DARTS-SIC for neural architecture search, the best parameters are learned from the search space, and the best neural architecture is determined; the target sonar images of each class in the
training set, the validation set and the
test set are input into the best neural architecture, the model is retrained and tested, the
test set recognition accuracy of each class of target is counted, and the final
classification result is obtained. The application can effectively solve the problems of low efficiency of the model designed by artificial and low model recognition accuracy in the traditional method, and has a wide application prospect.