文章基本信息

标题：Research on Convolutional Neural Network Model for Sonar IMAGE Segmentation
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作者：Jiao Shengxi ; Zhao Chunyu ; Xin Ye 等
期刊名称：MATEC Web of Conferences
电子版ISSN：2261-236X
出版年度：2018
卷号：220
DOI：10.1051/matecconf/201822010004
语种：English
出版社：EDP Sciences
摘要：The speckle noise of sonar images affects the human interpretation and automatic recognition of images seriously. It is important and difficult to realize the precision segmentation of sonar image with speckle noise in the field of image processing. Full convolution neural network (FCN) has the advantage of accepting arbitrary size image and preserving spatial information of original input image. In this paper, the image features are obtained by autonomic learning of convolutional neural network, the original learning rules based on the mean square error loss function is improved. Taking the pixel as the processing unit, the segmentation method based on FCN model with relative loss function(FCN-RLF) for small submarine sonar image is proposed, sonar image pixel-level segmentation is achievied. Experimental results show that the improved algorithm can improve the segmentation accuracy and keep the edge and detail of sonar image better. The proposed model has better ability to reject sonar image speckle noise.