Sonar image quality evaluation using deep neural network

Sonar technology plays an important role in the development of marine resources and military strategy. Due to the bad quality of underwater acoustics channels, the sonar images collected by sonar technology equipment are easily affected by various kinds of distortions. To obtain high‐quality sonar images, the authors devise a novel dual‐path deep neural network (DPDNN) to measure the quality of sonar images. In these two paths, the authors use a batch normalization layer to reduce the training time and use the skip operation to speed up the feature extraction . Based on the above two operations, the authors extract the microscopic and macroscopic structures of sonar images, respectively. Finally, a global average pooling layer and a fully connection layer are used to connect the above two paths. Experiments show that the authors' DPDNN achieves significant improvements in prediction performance and efficiency. The source code will be published in the near future.