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Sonar Image Target Detection and Recognition Based on Convolution Neural Network

机译:基于卷积神经网络的声纳图像目标检测与识别

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Recent advancements in deep learning offer an effective approach for the study in machine vision using optical images. In this paper, a convolution neural network is used to deal with the target task of sonar detection, and the performance of each neural network model in the sonar image detection and recognition task of underwater box and tire is compared. The simulation results show that the neural network method proposed in this paper is better than the traditional machine learning methods and SSD network models. The average accuracy of the proposed method for sonar image target recognition is 93%, and the detection time of a single image is only 0.3 seconds.
机译:深度学习的最新进步为使用光学图像进行机器视觉研究的有效方法。 在本文中,使用卷积神经网络来处理声纳检测的目标任务,以及在水下箱和轮胎的声纳图像检测和识别任务中的每个神经网络模型的性能。 仿真结果表明,本文提出的神经网络方法优于传统的机器学习方法和SSD网络模型。 Sonar图像目标识别的所提出方法的平均精度为93%,单个图像的检测时间仅为0.3秒。

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