信号处理2025，Vol.41Issue(11)：1826-1838,13.DOI:10.12466/xhcl.2025.11.008
基于类别自适应动态阈值与参数迁移学习的小样本声呐图像分类方法
Few-Shot Sonar Image Classification Method Based on Class-Adaptive Dynamic Threshold and Parameter Transfer Learning
摘要
Abstract
Sonar image classification is a key technology for underwater target detection and recognition,and it is widely applied in fields such as marine resource exploration and biological monitoring.Owing to the complex back-ground of the marine environment,the low resolution and severe noise of sonar images,and the large amount of data that remain unlabeled because they cannot be labeled in real time,challenges arise.In addition,owing to the high cost of sonar equipment and high degree of technical specialization,open-source and high-quality sonar image datasets are extremely scarce,and the number of samples of different underwater target categories in the dataset varies greatly,mak-ing it difficult to directly use machine learning for identification and classification.In this paper,we propose a few-shot sonar image classification method based on category-adaptive dynamic thresholds and parameter transfer learning to solve the problems of insufficient sonar image data and category imbalance and to address the difficulty for traditional deep learning models to achieve good generalization.First,leveraging the concept of parameter transfer learning,a lightweight AlexNet model was pre-trained on the large-scale ImageNet image dataset.Then,this model was fine-tuned using a labeled sonar image dataset to solve the few-shot problem of sonar images.Second,a category-adaptive dynamic threshold mechanism was designed.According to the distribution of different underwater target categories,the confi-dence threshold for recognizing sonar images is dynamically adjusted to screen appropriate pseudo-labeled data and alle-viate the training bias problem caused by class imbalance.Finally,the screened pseudo-labeled sonar data were fused with existing labeled data to construct a new training set for semi-supervised retraining.By reasonably setting the opti-mizer parameters,the robustness and generalization ability of the model were further improved.The simulation results show that on the real sonar image dataset,the proposed method can achieve an overall classification accuracy of 96.15%and an average F1 score of 93.48%when using only 21%of the labeled data.This confirms that the proposed method can achieve excellent classification performance under conditions of few-shot data and data category imbalance.关键词
声呐图像分类/小样本/迁移学习/类不平衡Key words
sonar image classification/few-shot/transfer learning/class imbalance分类
信息技术与安全科学引用本文复制引用
陈友淦,周明昭,涂申奥,赵矣昊..基于类别自适应动态阈值与参数迁移学习的小样本声呐图像分类方法[J].信号处理,2025,41(11):1826-1838,13.基金项目
国家自然科学基金面上项目(62271423) （62271423）
深圳市科技计划基础研究面上项目(JCYJ20230807091406013)The National Natural Science Foundation of China-General(62271423) （JCYJ20230807091406013）
Basic Research Program of Science and Technology of Shenzhen,China(JCYJ20230807091406013) （JCYJ20230807091406013）
