A light-weight neural network for marine acoustic signal recognition suitable for fiber-optic hydrophones

Fiber-optic hydrophones based on optical fiber sensing and communication technology have developed rapidly in recent years. With the advantages of high sensitivity, small size, and large dynamic range, they are easy to detect underwater acoustic signals, showing great potential in the field of marine ecological protection. Researchers can monitor the marine ecological environment by recognizing underwater optical fiber sensing signals collected by fiber-optic hydrophones in real-time. Deep learning models can achieve high recognition accuracy, but they usually have large amounts of parameters that makes them difficult to be deployed in mobile terminals of marine survey ships. To solve the key problem, this paper discussed the design of the Deep Light-Weight Attention Residual Network (DLA-ResNet). It introduces the depth-wise separable convolution (DSC) and the convolutional block attention module (CBAM) into ResNet18 to make its performance better. ResNet18 is a two-dimensional convolutional network (CNN) model applying residual blocks. Experiments verify that the accuracy of DLA-ResNet improved from ResNet18 is increased by about 2.04% and its size is reduced by 85.67%. The proposed scheme in this paper realizes an average recognition rate of 96.50% for eight types of underwater optical fiber sensing signals in the application of marine ecological environment monitoring. Moreover, its model size is only 6.12 MB and it only takes 27.90 ms, which is available to meet the needs of reliability and effectiveness.