联合迁移学习和深度学习的侧扫声呐沉船识别方法

Side Scan Sonar Combined With Transfer Learning and Deep Learning Shipwreck Identification Method

针对当前沉船侧扫声呐数据样本少、无法大量获取有标记的数据、小样本数据用于卷积神经网络模型正确识别率较低的问题,提出一种联合迁移学习和深度学习的沉船侧扫声呐图像识别方法。在不采用迁移学习的情况下,用沉船声呐样本数据集训练网络,将结果保存作为对照组。应用弱相关数据集,对网络进行训练,然后将网络参数迁移到新网络中,再使用沉船声呐样本数据集训练;应用强相关数据集重复上述步骤。在AlexNet网络结构上进行试验,试验结果表明:在未迁移的情况下,正确率为70.58%;应用弱相关数据集训练,正确率为74.51%;应用强相关数据集训练,正确率为80.39%。试验证明,迁移学习算法有利于提高小样本情况下卷积神经网络的正确率、泛化性。

Aiming at the problem that the current sunken side scan sonar data samples are few and it is impossible to obtain a large amount of labeled data,the small sample data is used for the problem of low correct recognition rate of the convolutional neural network model,and a joint migration learning and deep learning sunken side scan sonar is proposed.In the case of not using migration learning,use the shipwreck sonar sample data set to train the network and save the result as a control group;use the weakly correlated data set to train the network,and then transfer the net⁃work parameters to the new network,and then use the shipwreck sonar Sample data set training;apply the strong cor⁃relation data set to repeat the above steps.Experiments on the AlexNet network structure show that the accuracy rate is 70.58%without migration;the accuracy rate is 74.51%for training with weakly correlated data sets;and 80.39%for training with strong correlated data sets.Experiments show that the transfer learning algorithm is beneficial to im⁃prove the accuracy and generalization of the convolutional neural network in the case of small samples.