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徐英 , 谷雨 , 彭冬亮 , 等 . 面向合成孔径雷达图像任意方向舰船检测的改进YOLOv3模型 [J ] . 兵工学报 , 2021 , 42 ( 8 ): 1698 - 1707 . DOI: 10.3969/j.issn.1000-1093.2021.08.014 http://doi.org/10.3969/j.issn.1000-1093.2021.08.014 为实现合成孔径雷达(SAR)图像舰船检测能同时输出目标位置和方位角估计信息，提出基于改进YOLOv3的任意方向舰船目标检测模型。定义有利于模型参数回归稳定性的角度范围，根据垂直框和旋转框预测结果定义多任务损失函数。通过融合垂直框和旋转框预测结果进行目标方位角估计校正，以进一步提高检测性能。采用SAR舰船目标检测数据集(SSDD+)和高分辨率SAR图像数据集(HRSID)分别进行改进模型的性能测试和迁移测试。实验结果表明：对于SSDD+，当交并比为0.5时平均精度均值mAP_0.5达到了0.841；对HRSID进行迁移测试时，mAP_0.5能够达到0.530；当网络输入尺寸为416×416时处理一帧图像耗时约为25 ms；采用高分辨率可见光舰船数据集(HRSC2016)进行改进模型的适用性测试，mAP_0.5为0.888，超过了部分已知模型的精度；该改进模型适用于纯海洋背景下SAR图像中的舰船目标检测，能够满足舰船目标检测的实时性需求。

XU Y , GU Y , PENG D L , et al. An improved YOLOv3 model for arbitrary-oriented ship detection in SAR image [J ] . Acta Armamentarii , 2021 , 42 ( 8 ): 1698 - 1707 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.08.014 http://doi.org/10.3969/j.issn.1000-1093.2021.08.014 An improved YOLOv3 model for arbitrary-oriented ship detection is proposed to realize the simultaneous output of both position and aspect angle estimation information for synthetic aperture radar (SAR) ship detection. The scope of target’s aspect angle which is beneficial for the stability of model parameter regression is defined, and the multi-task loss function is defined based on the predictions of both vertical and rotated bounding boxes. The combinations of prediction results of both vertical and rotated bounding boxes are used to rectify target’s aspect angle estimation for improving the detection performance. The SAR ship detection dataset plus (SSDD+) and high resolution SAR images dataset (HRSID) are used to do performance and transferability tests for the proposed model. The experimental results demonstrate that, for SSDD+dataset, the mean average precision reaches 0.841 when intersection over union equals 0.5 (mAP_0.5); mAP_0.5 can reach 0.530 when HRSID is used to perform transferability tests; the proposed model takes about 25 milliseconds to process one frame when the input resolution of the model is 416×416. High resolution ship collection 2016 (HRSC2016) which is a visual ship recognition dataset is also used to verify the adaptability of the proposed model, and mAP_0.5 reaches 0.888, which is superior to some known models. The proposed model can be applied to detect SAR ships in pure sea background, and can meet real-time requirement of ship detection.