An Anchor-free Method Based on Context Information Fusion and Interacting Branch for Ship Detection in SAR Images

QU Haicheng; GAO Jiankang; LIU Wanjun; WANG Xiaona

doi:10.11999/JEIT201059

Volume 44 Issue 1

Jan. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(1): 380-389

QU Haicheng, GAO Jiankang, LIU Wanjun, WANG Xiaona. An Anchor-free Method Based on Context Information Fusion and Interacting Branch for Ship Detection in SAR Images[J]. Journal of Electronics & Information Technology, 2022, 44(1): 380-389. doi: 10.11999/JEIT201059

Citation:

QU Haicheng, GAO Jiankang, LIU Wanjun, WANG Xiaona. An Anchor-free Method Based on Context Information Fusion and Interacting Branch for Ship Detection in SAR Images[J]. Journal of Electronics & Information Technology, 2022, 44(1): 380-389. doi: 10.11999/JEIT201059

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An Anchor-free Method Based on Context Information Fusion and Interacting Branch for Ship Detection in SAR Images

doi: 10.11999/JEIT201059

School of Software, Liaoning Technical University, Huludao 125105, China

Funds: The Young Scientists Fund of National Natural Science Foundation of China (41701479), The Department of Education Fund Item (LJ2019JL010) of Liaoning Province, The Discipline Innovation Team of Liaoning Technical University (LNTU20TD-23)

Received Date: 2020-12-16
Rev Recd Date: 2021-05-27

Available Online: 2021-08-27

Publish Date: 2022-01-10

Abstract

Abstract

Ship targets are sparsely distributed in Synthetic Aperture Radar (SAR) images, and the design of anchor frame has a great impact on the accuracy and generalization of existing SAR image target detection method based on anchor. Therefore, an anchor-free method based on context information fusion and interacting branch for ship detection in SAR images (named as CI-Net) is proposed. Considering the diversity of ship scale in SAR images, a context fusion module is designed in the feature extraction stage, integrate high and low levels of information in a bottom-up manner and refine the extracted features to be detected by combining with the target context information. Secondly, aiming at the problem of complex targets in the scene is not accurate, interacting branch module is put forward. In the detection phase, use classification branches optimization regression testing box is used, to improve the target frame’s precision. At the same time, the new Intersection over Union (IOU) is used on branches of the classification to improve detection network classification confidence, to inhibit detection box of low quality. Experimental results show that the proposed method achieves good detection results on both SSDD and SAR-Ship-Dataset, with Average Precision (AP) reaching 92.56% and 88.32%, respectively. Compared with other ship detection methods in SAR image, the proposed method not only has excellent performance in accuracy, but also has a faster detection speed after abandoning the complex calculation related to anchor frame. It also has a certain practical significance for real-time target detection in SAR image.
- Synthetic Aperture Radar (SAR),
- Ship detection,
- Anchor-free,
- Context information,
- Self-attention

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References(23)

References

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