数字海洋与水下攻防2024，Vol.7Issue(5)：481-487,7.DOI:10.19838/j.issn.2096-5753.2024.05.003
基于Openmax的水下声呐图像开放集分类
Open-set Classification of Sonar Images Based on Openmax
摘要
Abstract
With the rapid development of artificial intelligence technology,open-set recognition has been widely studied as an emerging field of classification problems.In this study,an open-set recognition task for the FLSMDD sonar dataset is designed to evaluate the ability of the Openmax algorithm to handle unknown class samples.Then it is compared with traditional Softmax algorithm and its thresholded variants.By combining residual networks and transfer learning techniques,the performance of different algorithms in terms of classification accuracy and robustness is tested.The results show that the Openmax algorithm has an overall accuracy improvement of 5%compared with Softmax,a macro-F1 improvement of 7%,and a weighted Macro-F1 increase of 6%,indicating that it has significant advantages in adaptability and robustness in handling unknown categories.Future research will explore optimizing algorithm to further improve recognition accuracy and processing efficiency.This study provides strong evidence for the development of open-set recognition technology and lays a theoretical and experimental foundation for the application of deep learning in a wider range of classification problems.关键词
水下声呐图像/深度学习/图像识别Key words
underwater sonar images/deep learning/image recognition分类
信息技术与安全科学引用本文复制引用
夏梓淇,张建磊,王晨,熊明磊,谢广明..基于Openmax的水下声呐图像开放集分类[J].数字海洋与水下攻防,2024,7(5):481-487,7.基金项目
国家自然科学基金面上项目"面向信息生态复杂性的群体博弈与协作动力学研究"(62073174). （62073174）
