SONAR IMAGE RECOGNITION BASED ON MACHINE LEARNING FRAMEWORK
Keywords: Sonar data, deep learning, Caffe framework, Inception-Resnet-v2 model, migration learning
Abstract. In order to improve the robustness and generalization ability of model recognition, sonar images are enhanced by preprocessing such as conversion coordinates, interpolation, denoising and enhancement, and the transfer learning method under the Caffe framework of MATLAB as an interface is used respectively (mainly composed of 8 layers of network structure, including 5 convolutional layers and 3 full chain layers) And the transfer learning method under the Python deep learning framework Inception-Resnet-v2 model for sonar image training and recognition. First of all, part of the sonar image dataset (derived from the 2021 National Robot Underwater Competition online competition data), using MATLAB as the interface Caffe framework, the sonar image is trained to obtain a training model, and then through parameter adjustment, the convolutional neural network model of sonar image automatic recognition is obtained, and the transfer learning method can use less sonar image data to solve the problem of insufficient sonar image data, and then make the training achieve a higher recognition rate in a shorter time. When the training data is randomly sampled for testing, the sonar data recognition model based on the Caffe framework is quickly and fully recognized, and the recognition rate can reach 92% when the test sample does not participate in the training of sonar image data; The transfer learning method under the Inception-Resnet-v2 model of python deep learning framework is used to train recognition on sonar images, and the recognition rate reaches about 97%. Using the two models in this paper, it is feasible to identify sonar images with high recognition rate, which is much higher than traditional recognition methods such as SVM classifiers, and the two sonar image data recognition models based on deep learning have better recognition ability and generalization ability.