水下无人系统学报2025，Vol.33Issue(2)：238-248,11.DOI:10.11993/j.issn.2096-3920.2024-0165
基于自训练YOLO 11模型的低虚警率声呐图像目标检测方法
A Sonar Image Target Detection Method with Low False Alarm Rate Based on Self-Trained YOLO11 Model
摘要
Abstract
Autonomous detection of sonar image targets is a key technology for unmanned undersea systems,but it faces the challenge of high false alarm rates in practical applications,which limits the quality and efficiency of mission execution by unmanned underwater systems.In this paper,an underwater target detection method based on the YOLO11 model was designed,and a false alarm rate detection method by self-training a deep learning detector on sonar images was proposed to reduce the false alarm rate.This method automatically generated proxy classification tasks based on the sonar image target detection dataset and improved the deep learning detector's learning of target and background features through pre-training,enhancing the detector's ability to distinguish between targets and backgrounds and thereby reducing the false alarm rate.Experimental results demonstrate that when the detector's confidence is set to the value corresponding to the maximum F1-score,the YOLO11 detector trained using the proposed method can reduce the false alarm rate by 11.60%compared to traditional transfer learning methods while achieving a higher recall rate.This method improves the generalization of the deep learning detector without using external datasets,providing an efficient self-training approach for underwater target detection scenarios with small sample sizes.关键词
水下目标检测/虚警率/声呐图像处理/深度学习Key words
underwater target detection/false alarm rate/sonar image processing/deep learning分类
武器工业引用本文复制引用
韩婧祺,南明星,张鹏,陈佳杰,胡正良..基于自训练YOLO 11模型的低虚警率声呐图像目标检测方法[J].水下无人系统学报,2025,33(2):238-248,11.基金项目
国家自然科学基金项目资助(62401601). （62401601）
