声呐是用声波探测海洋的主要设备，自诞生以来，一直作为水下信息探测、定位和通信的主要工具。获取的声呐数据以图像的形式将目标信息显示出来，由于受海洋信道的影响和接收基阵的限制，声呐图像的处理缺乏完全可靠的模型方法。深度学习在近年来广泛应用于图像识别和目标识别领域，本文基于声呐图像的主要特征表现，提出一种基于卷积神经网络的声呐图像目标识别方法。使用中值滤波对声呐图像进行滤波处理，随后选用Canny边缘检测算法和霍夫变换进行白线检测，基于自适应阈值化图像分割算法分割出的目标，选用卡尔曼滤波器方法实现目标跟踪。最后对于跟踪的目标选用卷积神经网络进行分类识别，对不同的声呐图像目标获得了较高的识别准确率。
Sonar is the main equipment that used sound waves to detect the ocean. Since its birth, it has been used as the main tool for underwater information detection, positioning and communication. The acquired sonar data displays the target information in the form of images. Due to the influence of the ocean channel and the limitation of the receiving array, the processing of sonar images lacks a completely reliable model method. Deep learning has been widely used in the field of image recognition and target recognition in recent years, based on the main characteristics of sonar images, this paper proposes a sonar image target recognition method based on convolutional neural network. First, use Median filtering to filter the sonar image, and then use Canny edge detection algorithm and Hough transform to detect white lines. Based on the target segmented by the adaptive thresholding image segmentation algorithm, the Kalman filter method is selected to achieve target tracking. Finally, a convolutional neural network is used for classification and recognition of the tracked target. Obtained a high recognition accuracy rate for different sonar image targets.
2020,42(12): 133-136 收稿日期：2020-08-25
DOI：10.3404/j.issn.1672-7649.2020.12.026
分类号：TN911.73
作者简介：张家铭(1993-),男,硕士研究生,主要从事声学、目标识别和深度学习方面的研究工作
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