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Comparison of two deep learning methods for ship target recognition with optical remotely sensed data

  • S.I. : Higher Level Artificial Neural Network Based Intelligent Systems
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Abstract

As an important part of modern marine monitoring systems, ship target identification has important significance in maintaining marine rights and monitoring maritime traffic. With the development of artificial intelligence technology, image detection and recognition based on deep learning methods have become the most popular and practical method. In this paper, two deep learning algorithms, the Mask R-CNN algorithm and the Faster R-CNN algorithm, are used to build ship target feature extraction and recognition models based on deep convolutional neural networks. The established models were compared and analyzed to verify the feasibility of target detection algorithms. In this study, 5748 remote sensing maps were selected as the dataset for experiments, and two algorithms were used to classify and extract warships and civilian ships. Experiments showed that for the accuracy of ship identification, Mask R-CNN and Faster R-CNN reached 95.21% and 92.76%, respectively. These results demonstrated that the Mask R-CNN algorithm achieves pixel-level segmentation. Compared with the Faster R-CNN algorithm, the obtained target detection effect is more accurate, and the performance in target detection and classification is better, which reflects the great advantage of pixel-level recognition.

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Acknowledgements

This work was supported by National Key R&D Program of China (2018YFC1407400) and the National Natural Science Foundation of China (No. 51678391), Major Research on Philosophy and Social Sciences of the Ministry of Education of China(No. 19JZD056 and No. 2018JZD059) for their funding of this research.

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Authors and Affiliations

  1. School of Marine Science and Technology, Tianjin University, Tianjin, 300072, China

    Dianjun Zhang & Jie Zhan

  2. Key Laboratory of Department of Culture and Tourism of Information Technology of Architectural Heritage Inheritance School of Architecture, Tianjin University, Tianjin, 300072, China

    Lifeng Tan

  3. College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin, 150001, China

    Yuhang Gao

  4. Faculty of Geodesy, University of Zagreb, 10 000, Zagreb, Croatia

    Robert Župan

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  1. Dianjun Zhang
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  2. Jie Zhan
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  3. Lifeng Tan
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  4. Yuhang Gao
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  5. Robert Župan
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Correspondence to Lifeng Tan.

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Zhang, D., Zhan, J., Tan, L. et al. Comparison of two deep learning methods for ship target recognition with optical remotely sensed data. Neural Comput & Applic 33, 4639–4649 (2021). https://doi.org/10.1007/s00521-020-05307-6

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  • DOI: https://doi.org/10.1007/s00521-020-05307-6

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