MATEC Web Conf.
Volume 220, 20182018 The 2nd International Conference on Mechanical, System and Control Engineering (ICMSC 2018)
|Number of page(s)||8|
|Section||Modern information technology and application|
|Published online||29 October 2018|
Research on Convolutional Neural Network Model for Sonar IMAGE Segmentation
1 Northeast Electric Power University, Advanced Sensor Technology Institute, 132011 Jilin Jilin, China
2 Northeast Electric Power University, Fault Diagnosis Institute, 132011 Jilin Jilin, China
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.
© The Authors, published by EDP Sciences, 2018
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