Sonar image recognition of underwater target based on convolutional neural network

基于卷积神经网络的水下目标声呐图像识别方法

School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China

Received: 5 August 2020

Abstract

Underwater target recognition is one core technology of underwater unmanned detection. To improve the accuracy of underwater automatic target recognition, a sonar image recognition method based on convolutional neural network was proposed and the underwater target recognition model was established according to the characteristics of sonar images. Firstly, the sonar image was segmented and clipped with a saliency detection method to reduce the dimension of input data, and to reduce the interference of image background to the feature extraction process. Secondly, by using stacked convolutional layers and pooling layers, the high-level semantic information of the target was automatically learned from the input sonar image, to avoid damaging the effective information caused by extracting image features manually. Finally, the spatial pyramid pooling method was used to extract the multi-scale information from the sonar feature maps, which was to make up for the lack of detailed information of sonar images and solve the problem caused by the inconsistent size of input images. On the collected sonar image dataset, the experimental results show that the target recognition accuracy of the present method can recognize underwater targets more accurately and efficiently than the conventional convolutional neural networks.

摘要

水下目标识别是水下无人探测的一项核心技术，为提高水下自动目标识别准确率，提出基于卷积神经网络的目标声呐图像识别方法，针对声呐图像特点，设计了融合图像显著区域分割和金字塔池化的水下目标识别模型。基于流形排序显著性检测方法分割和裁剪图像，减小输入数据维度并减少图像背景对目标特征提取过程的干扰；通过堆叠卷积层和池化层，从原始声呐图像中自动学习目标的高层语义信息，避免人工提取图像特征对有效信息的破坏；提出采用空间金字塔池化方法提取特征图中的多尺度信息，弥补声呐图像细节信息少的缺陷，同时解决输入图像尺寸不一致的问题。结果表明，设计的卷积神经网络模型在实测声呐图像数据集上能够比常规卷积神经网络更准确、更快速地识别水下目标。