TY - GEN
T1 - Target Recognition Method of Sonar Image Based on Deep Learning
AU - Gao, Renjie
AU - Yan, Yongsheng
AU - Liu, Xue
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The target detection and recognition technology of sonar images plays an important role in the field of marine environment monitoring. The traditional method mainly uses CNN to study sonar image recognition. However, the lack of data volume and the limitations of the CNN network itself reduce the accuracy of sonar image classification. To address the above issues, this paper first uses the DCGAN network for data augmentation. Data expansion generates fake images based on the collection of public data sets from the network and completes the expansion of the data set. Secondly, this paper uses ResNet network and DenseNet network to replace the traditional CNN network and uses focal loss to replace the traditional cross-entropy loss function. The model has a good classification effect for the data set in this paper. The classification accuracy of the improved ResNet network is 77%, and the classification accuracy of the improved DenseNet network is 84.1%.
AB - The target detection and recognition technology of sonar images plays an important role in the field of marine environment monitoring. The traditional method mainly uses CNN to study sonar image recognition. However, the lack of data volume and the limitations of the CNN network itself reduce the accuracy of sonar image classification. To address the above issues, this paper first uses the DCGAN network for data augmentation. Data expansion generates fake images based on the collection of public data sets from the network and completes the expansion of the data set. Secondly, this paper uses ResNet network and DenseNet network to replace the traditional CNN network and uses focal loss to replace the traditional cross-entropy loss function. The model has a good classification effect for the data set in this paper. The classification accuracy of the improved ResNet network is 77%, and the classification accuracy of the improved DenseNet network is 84.1%.
KW - deep learning
KW - generative adversarial nets
KW - residual neural network
KW - sonar image
UR - http://www.scopus.com/inward/record.url?scp=85184851150&partnerID=8YFLogxK
U2 - 10.1109/ICSPCC59353.2023.10400377
DO - 10.1109/ICSPCC59353.2023.10400377
M3 - 会议稿件
AN - SCOPUS:85184851150
T3 - Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
BT - Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
Y2 - 14 November 2023 through 17 November 2023
ER -
