针对浒苔目标检测的全局背景强化的位置蒸馏方法  

Distillation method based on global background strengthen for Enteromorpha prolifera target detection

作　　者：刘兵 刘宇 金凤学 邹一波 葛艳[3] 赵林林 LIU Bing;LIU Yu;JIN Fengxue;ZOU Yibo;GE Yan;ZHAO Linlin(Key Laboratory of Ecological Prewarning,Protection and Restoration of Bohai Sea,Ministry of Natural Resources,Qingdao 266033,China;Three Gorges New Energy Yancheng Dafeng Co.,Ltd.,Yancheng 224199,China;The School of Information,Shanghai Ocean University,Shanghai 201308,China)

机构地区：[1]自然资源部生态预警与保护修复重点实验室,山东青岛266033 [2]三峡新能源盐城大丰有限公司,江苏盐城224199 [3]上海海洋大学信息学院,上海201308

出　　处：《测绘通报》2024年第6期19-23,共5页Bulletin of Surveying and Mapping

基　　金：自然资源部生态预警与保护修复重点实验室开放基金(2022105)。

摘　　要：浒苔检测是目前海洋环境智能监测领域研究的重要课题之一。为了有效解决传统浒苔检测方法存在的训练样本需求大的问题,本文提出了一种全局背景强化的位置蒸馏模型(GBS-LD)。通过引入全局上下文模块和背景蒸馏损失分支,解决了原始位置蒸馏方法在建模背景特征上的不足,在复杂海洋环境下有效提高了浒苔检测系统的稳健性。在浒苔检测数据集中,本文模型具有较高的准确性和实时性,为海洋智能监测提供了重要参考。The detection of Enteromorpha prolifera stands as a pivotal research area within the realm of intelligent marine environment monitoring.Addressing the challenge posed by the substantial training sample requirements inherent in conventional methods of Enteromorpha prolifera detection,this paper proposes GBS-LD model.By introducing a global context module and background distillation loss branch,the shortcomings of the original position distillation method in modeling background features are solved,effectively improving the robustness of detection system in complex marine environments.Our proposed model has achieved high accuracy and real-time performance in the dataset of Enteromorpha prolifera,providing important reference for intelligent monitoring of marine.

关 键 词：浒苔 位置蒸馏 全局背景强化 目标检测 深度学习 

分 类 号：P237[天文地球—摄影测量与遥感]