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DeepSORT-OCR: Design and Application Research of a Maritime Ship Target Tracking Algorithm Incorporating Hull Number Features

Author

Listed:
  • Jing Ma

    (China Ship Research and Development Academy, Beijing 100101, China)

  • Xihang Su

    (China Ship Research and Development Academy, Beijing 100101, China)

  • Kehui Xu

    (China Ship Research and Development Academy, Beijing 100101, China)

  • Hongliang Yin

    (China Ship Research and Development Academy, Beijing 100101, China)

  • Zhihong Xiao

    (School of Automation, University of Science and Technology Beijing, Beijing 100083, China)

  • Jiale Wang

    (China Ship Research and Development Academy, Beijing 100101, China)

  • Peng Liu

    (China Ship Research and Development Academy, Beijing 100101, China)

Abstract

Maritime ship target tracking plays an important role in applications such as maritime patrol and maritime surveillance. However, complex sea conditions, similar target appearances, and long-distance imaging often lead to target identity confusion and unstable trajectories. To address these issues, in this paper, a ship multi-object tracking algorithm, DeepSORT-OCR, that integrates hull number semantic features is proposed. Based on the YOLO detection framework and the DeepSORT tracking architecture, a CBAM-ResNet network is introduced to enhance the representation of ship appearance features. An Inner-SIoU metric is adopted to improve the geometric matching of slender ship targets, while an LSTM-Adaptive Kalman Filter is employed to model the nonlinear motion patterns of ships and improve trajectory prediction stability. In addition, a Hull Number Feature Extraction module is designed in order to recognize ship hull numbers using OCR and match them with a hull number database. The extracted hull number semantic features are dynamically fused with visual appearance features to strengthen identity constraints during target association. The experimental results show that the proposed method achieves an MOTA of 66.53% on the MOT16 dataset, representing an improvement of 5.13% over DeepSORT. On the self-constructed maritime ship dataset, the method achieves an MOTA of 70.89% and an MOTP of 80.84%. Furthermore, on the hull-number subset, the MOTA further increases to 77.18%, an improvement of 7.31% compared with DeepSORT, while the number of ID switches is significantly reduced. In addition, experiments conducted on pure real data, pure synthetic data, and cross-domain evaluation settings demonstrate the stability and strong generalization capability of the proposed algorithm under different data distributions. The proposed method effectively improves the stability and identity consistency of ship multi-object tracking in complex maritime environments.

Suggested Citation

  • Jing Ma & Xihang Su & Kehui Xu & Hongliang Yin & Zhihong Xiao & Jiale Wang & Peng Liu, 2026. "DeepSORT-OCR: Design and Application Research of a Maritime Ship Target Tracking Algorithm Incorporating Hull Number Features," Mathematics, MDPI, vol. 14(6), pages 1-28, March.
  • Handle: RePEc:gam:jmathe:v:14:y:2026:i:6:p:1062-:d:1900182
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