Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (6): 1935-1941.DOI: 10.11772/j.issn.1001-9081.2023060859

Special Issue: 多媒体计算与计算机仿真

• Multimedia computing and computer simulation • Previous Articles     Next Articles

Ship identification model based on ResNet50 and improved attention mechanism

Yuanjiong LIU1,2,3(), Maozheng HE1,2,3, Yibin HUANG4, Cheng QIAN4   

  1. 1.Key Laboratory of Metallurgical Equipment and Control,Ministry of Education (Wuhan University of Science and Technology),Wuhan Hubei 430081,China
    2.Hubei Provincial Key Laboratory of Mechanical Transmission and Manufacturing Engineering (Wuhan University of Science and Technology),Wuhan Hubei 430081,China
    3.Institute of Precision Manufacturing,Wuhan University of Science and Technology,Wuhan Hubei 430081,China
    4.Jiangsu Jianlong Shore Power Technology Company Limited,Yixing Jiangsu 214200,China
  • Received:2023-07-04 Revised:2023-08-15 Accepted:2023-08-23 Online:2023-09-11 Published:2024-06-10
  • Contact: Yuanjiong LIU
  • About author:HE Maozheng, born in 1997, M. S. candidate. His research interests include machine vision.
    HUANG Yibin, born in 1975, M. S., senior engineer. His research interests include image recognition.
    QIAN Cheng, born in 1988, M. S., senior engineer. His research interests include electrical automation.
  • Supported by:
    Intellectual Property Application Demonstration Project in Hubei Province in 2020(202014)

基于ResNet50和改进注意力机制的船舶识别模型

刘源泂1,2,3(), 何茂征1,2,3, 黄益斌4, 钱程4   

  1. 1.冶金装备及其控制教育部重点实验室(武汉科技大学), 武汉 430081
    2.机械传动与制造工程湖北省重点实验室(武汉科技大学), 武汉 430081
    3.武汉科技大学 精密制造研究院, 武汉, 430081
    4.江苏健龙岸电科技有限公司, 江苏 宜兴 214200
  • 通讯作者: 刘源泂
  • 作者简介:何茂征(1997—),男,河南濮阳人,硕士研究生,主要研究方向:机器视觉
    黄益斌(1975—),男,江苏宜兴人,高级工程师,硕士,主要研究方向:图像识别
    钱程(1988—),男,江苏宜兴人,高级工程师,硕士,主要研究方向:电气自动化。
  • 基金资助:
    2020年湖北省知识产权运用示范项目(202014)

Abstract:

Automatic identification of marine ships plays an important role in alleviating the pressure of marine traffic. To address the problem of low automatic ship identification rate, a ship identification model based on ResNet50 (Residual Network50) and improved attention mechanism was proposed. Firstly, a ship data set was made by ourselves, and divided into the training set, the verification set and the test set, which were augmented by blurring and adding noise. Secondly, an improved attention module — Efficient Spatial Pyramid Attention Module (ESPAM) and ship type recognition model ResNet50_ESPAM were designed. Finally, the ResNet50_ESPAM was trained, verified and compared with other commonly used neural network models using ship data sets. The experimental results show that in the verification set, the highest accuracy of ResNet50_ESPAM is 95.5%, and the initial accuracy is 81.2%; compared with AlexNet(Alex Krizhevsky Network), GoogleNet (Google Inception Net), ResNet34(Residual Network34), ResNet50 and ResNet50_CBAM (ResNet50_Convlutional Block Attention Module), the maximum accuracy of the model validation set increases by 5.1, 4.9, 2.6, 1.6 and 1.4 percentage points respectively, and the initial accuracy of the validation set increases by 49.4, 44.7, 27.7, 3.0 and 2.1 percentage points respectively, indicating that ResNet50_ESPAM has a high recognition accuracy in ship type recognition, and the improved attention module ESPAM is highly effective.

Key words: image processing, deep learning, residual network, attention mechanism, ship identification

摘要:

海洋船舶的自动识别对缓解海上交通压力起着重要作用。针对当前船舶自动识别率较低的问题,提出一种基于ResNet50(Residual Network50)和改进注意力机制的船舶识别模型。首先,自制船舶数据集并划分为训练集、验证集和测试集,采用模糊、增加噪声等方法得到增强数据集;其次,设计改进注意力模块——高效空间金字塔注意力模块(ESPAM)和船舶种类识别模型ResNet50_ESPAM;最后,将ResNet50_ESPAM与其他常用的神经网络模型对船舶数据集进行训练验证并对比。实验结果表明,ResNet50_ESPAM在验证集最高准确率为95.5%,验证集初始准确率为81.2%,与AlexNet(Alex Krizhevsky Network)、GoogleNet(Google Inception Net)、ResNet34(Residual Network34)、ResNet50和ResNet50_CBAM(ResNet50_ Convlutional Block Attention Module)等模型相比,模型验证集最高准确率分别提升了5.1、4.9、2.6、1.6和1.4个百分点,验证集初始准确率分别提升了49.4、44.7、27.7、3.0和2.1个百分点。实验结果表明ResNet50_ESPAM在船舶种类识别方面具有较高的识别精度,验证了改进的注意力模块ESPAM的有效性。

关键词: 图像处理, 深度学习, 残差网络, 注意力机制, 船舶识别

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