Side Scan Sonar Shipwreck Image Recognition Algorithm based on Transfer Learning and Deep Learning

Current underwater shipwreck side scan sonar samples are few and difficult to label. With small sample sizes, their image recognition accuracy with a convolutional neural network model is low. In this study, we proposed an image recognition method for shipwreck side scan sonar that combines transfer...

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Bibliographic Details
Published inE3S Web of Conferences Vol. 290; p. 2020
Main Authors Zhang, Boyu, Wang, Xiao, Li, Shudong, Yang, Jinghua
Format Journal Article Conference Proceeding
LanguageEnglish
Published Les Ulis EDP Sciences 01.01.2021
Subjects
Accuracy
Algorithms
Artificial neural networks
Computer architecture
Correlation
Deep learning
Machine learning
Neural networks
Object recognition
Shipwrecks
Side scan sonar
Sonar
Training
Transfer learning
Underwater
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Summary:Current underwater shipwreck side scan sonar samples are few and difficult to label. With small sample sizes, their image recognition accuracy with a convolutional neural network model is low. In this study, we proposed an image recognition method for shipwreck side scan sonar that combines transfer learning with deep learning. In the non-transfer learning, shipwreck sonar sample data were used to train the network, and the results were saved as the control group. The weakly correlated data were applied to train the network, then the network parameters were transferred to the new network, and then the shipwreck sonar data was used for training. These steps were repeated using strongly correlated data. Experiments were carried out on Lenet-5, AlexNet, GoogLeNet, ResNet and VGG networks. Without transfer learning, the highest accuracy was obtained on the ResNet network (86.27%). Using weakly correlated data for transfer training, the highest accuracy was on the VGG network (92.16%). Using strongly correlated data for transfer training, the highest accuracy was also on the VGG network (98.04%). In all network architectures, transfer learning improved the correct recognition rate of convolutional neural network models. Experiments show that transfer learning combined with deep learning improves the accuracy and generalization of the convolutional neural network in the case of small sample sizes.
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202129002020