Research on sonar image few-shot classification based on deep learning - CHEN Yule; LI Bo; LIANG Hong; YANG Changsheng (Xibei Gongye Daxue Xuebao, 2022)
The underwater environment is complex and diverse, which makes it difficult to evolve traditional methods such as manually extracting features from blurred images. What's more, sonar images are so hard to be obtained that their number is far less than optical images, this case usually is called as few-shot, which leads to over fitting and low recognition accuracy of networks for sonar image classification. Based on the established sonar image data set after image preprocessing, a sonar image few-shot classification method with multi strategy optimization fusion is proposed in this paper. It is an improved label smooth regularization method with category preferences that can optimize the labels of training data and reduce the self-confidence of the network. And then based on the fine-tuning method in migration learning, some parameters of pre-learned models from optical images domain are utilized to help improve the performance in the sonar images domain. Finally, all the above three optimization strategies are combined. The simulation experiments in this study conclude that the optimal recognition accuracy can increase to 96.94%, which proves the multi strategy fusion can effectively suppresses the overfitting phenomenon and accurately classifies sonar images in the case of few-shot.