Residual unit, network, target identification method, system and device and medium

Abstract

The invention discloses a residual unit, a network, a target recognition method, system and device and a medium, and relates to the field of underwater acoustic recognition, deep learning and artificial intelligence, and the multi-scale residual unit comprises a residual network architecture composed of a batch normalization layer, an activation layer and a convolution layer, and is characterized in that the residual network architecture is used for processing a one-dimensional original audio waveform; the residual error network architecture is provided with at least one convolution middle layer, the convolution middle layer is formed by a plurality of convolution layers in parallel, and at least one soft threshold value operation unit is arranged between the convolution middle layer and the tail end of the residual error network architecture and is used for carrying out soft threshold value calculation and filtering. According to the invention, the underwater sound one-dimensional signal waveform can be effectively sensed.

Description

technical field [0001] The present invention relates to the fields of underwater acoustic recognition, deep learning and artificial intelligence, in particular, to a residual unit and a network, a target recognition method and system, a device and a medium. Background technique [0002] Underwater acoustic target recognition is to use the target radiation sound signal collected by the hydrophone to identify the target. It plays an important role in ocean transportation and waterway management. Due to the complexity of the ocean acoustic environment, the collected radiation signals are often accompanied by a large amount of interference noise. Because these noises are not explicit or semantic, it is difficult to label and automatically classify them. How to improve the automatic detection and classification performance of signals is still a challenging problem. [0003] The traditional method is to extract artificially designed features from the original audio data, then u...

AI Technical Summary

Problems solved by technology

But this method will lose the fine structure information of the original signal waveform

In addition, the generation of time-frequency diagrams is usually limited by parameters such as the window size and window interval step of the Fourier transform.

On the one hand, determining the appropriate transformation parameters requires prior knowledge, and the time and frequency resolution cannot be optimal at the same time

On the other hand, once the parameters are determined, the resolution of the generated spectrum is fixed accordingly

For end-to-end models with a fixed input size, this results in loss of additional resolution information

This becomes a bottleneck for spectrum-based identification methods

Images