目的 船舶在合成孔径雷达（synthetic aperture radar， SAR）图像中的检测是研究热点，但目前适合近岸舰船检测的方法并不多。在SAR图像中，近岸舰船受到岸上建筑物的干扰严重，尤其是对于排列紧密的近岸船舶来说，其对比度相似，很难区分船舶与背景。为解决近岸舰船检测困难问题，提出了一种基于加权双向注意金字塔网络的近岸舰船检测方法。方法 本文在FCOS （fully convolutional one-stage）网络的基础上提出了一种新的双向特征金字塔网络。将卷积注意力机制模块（convolutional block attention module，CBAM）与金字塔网络的每个特征图进行连接，提取丰富的语义信息特征；借鉴PANet （path aggregation network）的思想，添加自下而上的金字塔模块，突出不同尺度船舶的显著特征。最后提出了一种加权特征融合方式，使特征图提取的特征信息的着重点不同，提高舰船检测精度。结果 本文在公开的SAR图像舰船数据集SSDD （SAR ship detection dataset）上进行实验。实验结果表明，相比原FCOS方法，本文方法的检测精度提高了9.5%；与对比方法相比，本文方法在同等条件下的检测精度达到90.2%。在速度方面，本文方法比SSD提高0.6 s，比Faster R-CNN （region convolutional neural network）提高1.67 s，明显优于对比方法。结论 本文通过改进特征网络和特征融合方式，提高了算法对SAR图像舰船目标检测中背景复杂、排列紧密的近岸舰船目标的定位效果，有效增强了对舰船目标定位的准确性。

Objective Synthetic aperture radar (SAR) is an active sensor that uses microwave remote sensing technology. Compared with visible and infrared sensors, it is not limited by light, weather, and climate conditions and has all-weather and multi-angle data acquisition capability. With the development of SAR imaging technology, SAR image has been widely used in the military field for intelligence detection, navigation guidance, ocean ship detection, etc. Through the detection of ship target in the SAR image, the ship information of the sea surface, port, and other locations can be obtained quickly, which improves border prevention ability. The traditional methods of SAR ship detection have difficulty in detecting small-scale ships and avoiding the interference of inshore complex background. Moreover, the quality of SAR images needs to be high, and the images need to be preprocessed before detection. In addition, the robustness and generalization of most SAR images are not good enough for specific scenes, and they are susceptible to speckle noise, which has certain limitations. With the development of artificial intelligence, machine learning has been introduced into the SAR image target detection field. Deep learning is an enhanced version of machine learning. Recently, it has been gradually applied for ship detection in SAR images, but some problems need to be addressed. First, the near shore ships are seriously disturbed by the buildings on the shore. The existing detection methods cannot effectively distinguish the ship target from the background, so it is easy to mix the background with the ship target or mistake the target for the background, resulting in missed detection. Second, the existing algorithm cannot accurately locate the closely arranged ship targets, and the positioning effect is poor. It is easy to regard multiple targets as one target, which leads to wrong detection, resulting in low accuracy of detection results or high constant false alarm rate. To solve these problems, this study proposes a detection method based on bidirectional attention feature pyramid network. Method Experiments are conducted on the SAR ship detection dataset (SSDD) of the public SAR image ship dataset. This dataset comes from the Naval Aviation University, which is a common dataset in this field in China. Affine, fuzzy, and noisy data enhancement operations are performed to improve the generalization ability of the training model. A new bidirectional feature pyramid network is proposed based on the original fully convolutional one-stage (FCOS) object detection network. By connecting the attention mechanism module with each feature graph of the pyramid network, the features of a large amount of semantic information can be extracted. At the same time, we use convolutional block attention module (CBAM) to refine the stitched feature map and combine the idea of path aggregation network for instance segmentation. The bottom-up pyramid module is added to further highlight the prominent features of targets at different scales, so as to improve the ability of the network to accurately locate ship targets in a complex background. Then, a weighted feature fusion method is proposed, which makes the feature information extracted from different feature maps to have different emphases and then combines the salient features with the global non-fuzzy features to improve the ship detection accuracy. Finally, the fused feature map is fed back to the detection network to obtain the final detection result. In addition, a NVIDIA Titan RTX device is used for the experimental platform in this study, and the operating system is Ubuntu 18.04. The batch size of the training is set to the initialization model, the batch size of the training is 8, the total iteration number is 50 000, the initial learning rate is 0.001, the learning rate attenuation coefficient is 0.000 1, the 2 000 attenuation is once, and the kinetic parameters are analyzed at 0.9 mm. Result We compared the added modules and found that the added modules have different degrees of improvement compared with the original FCOS method. At the same time, we also compared the experimental results with three other existing models, including SSD, Faster R-CNN (region convolutional neural network), and original FCOS. Experimental results show that the detection accuracy of the proposed algorithm is 9.5% higher than that of the original FCOS method. The detection accuracy of this algorithm reached 90.2% under the same conditions, which was 14.09% higher than that of SSD and 8.5% higher than that of Faster R-CNN. The results show that the model algorithm can obtain accurate results. In addition, in terms of time, the proposed algorithm is 5 ms slower than FCOS. However, the detection speed was 1.6 times higher than that of SSD and 6 times higher than that of Faster R-CNN. In terms of speed, the detection speed of the proposed algorithm is obviously faster than that of other existing methods. From the comparison of experimental results, the proposed algorithm is not only good for the detection of small target ships in deep sea, but also good for the detection of near shore ship targets in the complex background, especially for closely arranged ship targets, with high accuracy of positioning effect. Conclusion By improving the network features and the feature fusion method, the ship detection algorithm in SAR images is improved on the premise of ensuring the same detection effect of deep-sea ships. Under complex background, the ship positioning effect is compact, and the accuracy of ship detection is effectively enhanced, which is superior to other ship detection methods based on SSDD.