基于YOLO框架的无锚框SAR图像舰船目标检测

面向合成孔径雷达(synthetic aperture radar, SAR)多目标检测应用, 提出了一种基于YOLO (you only look once) 框架的无锚框SAR图像舰船目标检测方法。该方法针对YOLOv3锚框需要预先设定且无法完美契合的弊端, 通过采用无锚框方法更好适应所检测目标的大小, 便于多尺度目标使用。在此基础上, 给CSPDarknet53网络增加了注意力机制作为特征提取网络, 然后经过能够增大感受野的改进特征金字塔网络(feature pyramid network, FPN)后, 把特征图传给无锚框检测头, 有效提升了目标类别和位置的预测精度。实验证明, 所提算法在公开SAR舰船数据集上平均精度比YOLOv3提高3.8%，达到了94.8%, 虚警率降低4.8%。

Anchor free SAR image ship target detection method based on the YOLO framework

For synthetic aperture radar (SAR) multi-target detection applications, this paper proposes an anchor free SAR image ship target detection method based on the you only look once (YOLO) framework. This method is aimed at the disadvantage that the YOLOv3 anchor needs to be preset and cannot fit perfectly. By adopting the anchor free method, it can better adapt to the size of the detected target and facilitate the use of multi-scale targets. On this basis, the attention mechanism is added to the CSPDarknet53 network as a feature extraction network, and then after an improved feature pyramid network (FPN) that can increases the receptive field, the feature map is transmitted to the anchor free detection head, which effectively improves the prediction precision of the target category and location. Experiments show that the improved algorithm has an average precision of 3.8% higher than YOLOv3 on the public SAR ship data set, reaching 94.8%, and the false alarm rate is reduced by 4.8%.