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An enhanced object detection network for ship target detection in SAR images

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Abstract

Deep learning techniques have made significant advancements in computer vision. The YOLO algorithm, a representative single-stage detection approach, has demonstrated remarkable results in detecting ship targets in SAR images. We introduce an enhanced ship target detection model for SAR images, utilizing the improved YOLOv7 object detection network. We incorporate the coordinate attention mechanism into the network to enable automatic detection and diagnosis of ship targets within SAR images. To enhance the robustness and positioning accuracy of the detection network, we replace the CIoU regression loss in YOLOv7 with the SIoU loss, reducing the complexity of the loss function. Additionally, we integrate the rotating target detection technology into the network to mitigate the impact of target overlap on detection results. Comprehensive experiments conducted on the Capella Space synthetic aperture radar datasets validate that the proposed methodology achieves superior performance in multiple evaluation metrics, including precision, recall, and mean average precision.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments. This article has been supported by the National Natural Science Foundation of China (61941113) and Science and Technology on Information System Engineering Laboratory (No: 05202104).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing, 210094, Jiangsu, China

    Haochen Zou

  2. Department of Computer Science and Software Engineering, Concordia University, 2155 Guy Street, Montreal, QC, H3H 2L9, Canada

    Haochen Zou & Zitao Wang

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  1. Haochen Zou
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HZ and ZW were involved in conceptualization; ZW helped with methodology, validation, resources, and project administration; and HZ was involved in software, formal analysis, investigation, data curation, writing—original draft preparation, writing—review and editing, and visualization. All authors have read and agreed to the published version of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Haochen Zou.

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Zou, H., Wang, Z. An enhanced object detection network for ship target detection in SAR images. J Supercomput 80, 17377–17399 (2024). https://doi.org/10.1007/s11227-024-06136-3

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