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研究成果"Contour Accentuation for Transfer Learning-Based Ship Recognition Method"发表于2020 Web Conference (formerly known as WWW conference)

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篇名:Contour Accentuation for Transfer Learning-Based Ship Recognition Method


作者:Chi-Hua Chen(陈志华), Yizhuo Zhang(张翊卓), Wenzhong Guo*, Mingyang Pan, Lingjuan Lyu, Chia-Yu Lin


来源:Proceedings of the 2020 Web Conference (formerly known as WWW conference)(TheWebConf 2020), Taipei, TaiwanApril 20th-24th, 2020. (ACM)(CCF Rank A)


年份:2020


DOI: https://doi.org/10.1145/3366424.3382697


文章摘要:


This study proposes a ship recognition system which includes intelligent bridge piers and a ship recognition server. The ship recognition server can analyse the contour features of ship images from intelligent bridge piers by the proposed contour accentuation method; the ship image with contour accentuation can be adopted as the inputs of transfer learning-based neural network for ship classification by the proposed transfer learning-based ship recognition method. In practical experiments, the results showed that the proposed transfer learning-based ship recognition method with contour accentuation can obtain higher accuracy, and the accuracy of the proposed method was 97.79%.


本研究提出了一个船舶识别系统,包括智能桥墩和船舶识别服务器。船舶识别服务器可以通过提出的轮廓重读方法分析来自智能桥墩的船舶图像的轮廓特征;提出了一种基于传递学习的船舶识别方法,将具有轮廓重读的船舶图像作为基于传递学习神经网络的船舶分类输入。实验结果表明,本文提出的基于迁移学习的轮廓重读船舶识别方法具有较高的识别精度,识别准确率为97.79%。