Research on sonar pulse recognition based on convolutional neural network

At present, convolutional neural networks have achieved good results in fields such as image classification, image detection, target segmentation, target tracking, and situation estimation. The network model is trained by batch data to process the image and video rapidly and efficiently. Due to a large number of common convolutional neural network models, the actual effects of the same data set under different network models are different. Therefore, to study and select the appropriate network in the field of sonar pulse image recognition, python is applied to building convolutional neural network structures of InceptionV3, InceptionResNetV2, mobilenetV3, VGG16, DenseNet121, and NASNetMobile. In addition, pulse sonar image simulation data set is used for experiments. The results show that mobilenetV3 is the most suitable network structure for pulse sonar image recognition considering both the running speed and accuracy.