Abstract
SAR ship detection based on deep learning has wide application, however there exist the following three problems for SAR ship detection. Firstly, the ships in the port are seriously disturbed by the onshore buildings. The existing detection methods cannot effectively distinguish the target from the background. Secondly, the algorithm cannot accurately locate the closely arranged ship targets. Finally, the ships in SAR images have a variety of scales, and the existing algorithms have poor positioning effect on ship targets of different scales. To solve the above problems, this paper proposes an object detection network which combines attention mechanism to enhance the network’s ability to accurately locate targets in complex background. To deal with the diversity of ship target scales, we propose a loss function that incorporates Generalized Intersection over Union (GIoU) loss to reduce the sensitivity of the algorithm to scale. The proposed algorithm achieves good results for ship target detection in complex backgrounds based on the extended SAR Ship Detection Dataset (SSDD), while maintaining a fast detection speed.
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Acknowledgments
This research was supported by the National Natural Science Foundation of China under grant No. 61773389, 61833016, 61573365.
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Chen, C., Hu, C., He, C., Pei, H., Pang, Z., Zhao, T. (2019). SAR Ship Detection Under Complex Background Based on Attention Mechanism. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_54
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DOI: https://doi.org/10.1007/978-981-13-9917-6_54
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