系统工程与电子技术 ›› 2023, Vol. 45 ›› Issue (10): 3049-3057.doi: 10.12305/j.issn.1001-506X.2023.10.07

• 电子技术 • 上一篇    

基于改进Swin Transformer的舰船目标实例分割算法

钱坤1,2,*, 李晨瑄3, 陈美杉1, 郭继伟2, 潘磊2   

  1. 1. 海军航空大学岸防兵学院, 山东 烟台 264000
    2. 中国人民解放军32127部队, 辽宁 大连 116100
    3. 航天工程大学研究生院, 北京 101416
  • 收稿日期:2022-05-09 出版日期:2023-09-25 发布日期:2023-10-11
  • 通讯作者: 钱坤
  • 作者简介:钱坤(1986—), 男, 助理讲师, 博士研究生, 主要研究方向为图像处理、模式识别
    李晨瑄(1996—), 女, 博士研究生, 主要研究方向为图像处理、模式识别
    陈美杉(1991—), 女, 助理工程师, 博士研究生, 主要研究方向为作战仿真推演
    郭继伟(1977—), 男, 讲师, 硕士, 主要研究方向为战役学
    潘磊(1979—), 男, 讲师, 硕士, 主要研究方向为战役学
  • 基金资助:
    装备预研领域基金(6140247030216JB14004)

Ship target instance segmentation algorithm based on improved Swin Transformer

Kun QIAN1,2,*, Chenxuan LI3, Meishan CHEN1, Jiwei GUO2, Lei PAN2   

  1. 1. College of Coastal Defense Force, Naval Aeronautical University, Yantai 264000, China
    2. Unit 32127 of the PLA, Dalian 116100, China
    3. Department of Graduate Management, Space Engineering University, Beijing 101416, China
  • Received:2022-05-09 Online:2023-09-25 Published:2023-10-11
  • Contact: Kun QIAN

摘要:

针对反舰武器图像制导目标实例分割精度低, 模型中上下文语义交互不充分, 特征融合推理速度慢, 数据集难易样本不均衡导致训练效果差等问题, 提出了一种基于改进滑动窗口的Transformer(shifted windows Transformer, Swin Transformer)的舰船目标实例分割算法。设计了局部增强感知模块用以拓展感受野, 加强语义交互能力; 采用反向特征金字塔网络进行特征融合, 提高算法处理速度; 使用在线困难样例挖掘, 改善数据集样本不均衡问题, 提升网络训练效果。实验结果表明, 改进后的算法相较基线算法在分割准确率上提升了1.5%, 在处理速度上提高了1.3%, 兼具精度和速度优势。

关键词: Swin Transformer, 反向特征金字塔, 在线困难样例挖掘, 舰船实例分割

Abstract:

Aiming at the problems of low segmentation accuracy of image guidance target instances of anti-ship weapons, insufficient semantic interaction of model context, slow inference speed of feature fusion, imbalance of hard and easy samples of data sets and poor training effect, an instance segmentation algorithm of ship target based on improved Swin Transformer is proposed. The local enhanced sensing block is designed to expand the receptive field and strengthen the ability of semantic interaction. The reverse feature pyramid network is used for feature fusion to improve the processing speed of the algorithm. Online Hard Example Mining is used to improve the sample imbalance of data set and improve the effect of network convergence. The experimental results show that, compared with the baseline algorithm, the improved algorithm improves the segmentation accuracy by 1.5% and the processing speed by 1.3%, with both accuracy and speed advantages.

Key words: shifted windows Transformer (Swin Transformer), reverse feature pyramid, online hard example mining (OHEM), ship instance segmentation

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