An efficient harbor ship detection method based on deep learning in high-resolution remote sensing images

Zengcheng Yu; Huigang Zheng

doi:10.1117/12.2684515

1 August 2023 An efficient harbor ship detection method based on deep learning in high-resolution remote sensing images

Zengcheng Yu, Huigang Zheng

Author Affiliations +

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127541N (2023) https://doi.org/10.1117/12.2684515
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

With the increasing prosperity of shipping, effective harbor management has become a requirement. High-resolution optical remote sensing images can effectively monitor and manage ships due to their rich spatial information, clear geometric information, and unique top-down view. However, harbor scenes usually have complex background information and densely distributed ship targets, these two factors increase the difficulty of the algorithm to detect ship targets in remote sensing harbor scenes. With respect to these two issues, this paper proposes a novel harbor ship detection method based on YOLOv6-s with finer-feature fusion which is called F-YOLOv6. First, Extract ship targets from complex ground information. By importing the backbone extraction network efficient re-parameterization (Efficient-Rep) of YOLOv6-s, its efficient extraction ability can help the algorithm to improve the detection accuracy. Second, to improve the detection performance of the algorithm for densely arranged ship targets, the finer pixel aggregation network (F-PAN) is constructed to obtain more ship object position information, and on this basis, it is integrated with the deep high-level semantic information to improve the robustness of the algorithm. Third, to further improve the timeliness of the algorithm, the detection efficiency of the algorithm is improved by deleting some redundant prediction headers. Finally, extensive experiments are carried out on the harbor ship detection (HSD) dataset, which is suitable for densely arranged ship detection, to verify the effectiveness of the proposed method. In addition, the method is verified by the DIOR public dataset, and its generalization is better than other benchmark algorithms.

Citation Download Citation

Zengcheng Yu and Huigang Zheng "An efficient harbor ship detection method based on deep learning in high-resolution remote sensing images", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127541N (1 August 2023); https://doi.org/10.1117/12.2684515

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KEYWORDS

Detection and tracking algorithms

Remote sensing

Object detection

Target detection

Head

Deep learning

Semantics

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