Volume 39, Number 2, April 2021
|Page(s)||285 - 291|
|Published online||09 June 2021|
Sonar image recognition of underwater target based on convolutional neural network
School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
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.
Key words: automatic target recognition / sonar image / convolutional neural network / saliency detection / spatial pyramid pooling
关键字 : 自动目标识别 / 声呐图像 / 卷积神经网络 / 显著性 / 金字塔池化
© 2021 Journal of Northwestern Polytechnical University. All rights reserved.
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