ID	原文	译文
53757	针对低信噪比条件下海面目标分类识别精度差的问题，该文提出了一种基于去噪卷积神经网络(Denoising convolutional neural network，DnCNN)的海面目标高分辨一维距离像(High Resolution Range Profile，HRRP)识别方法。	Aiming at the problem of poor target classification and recognition accuracy under low signal-to-noise ratio(SNR) conditions, a high resolution one-dimensional range profile(HRRP) recognition method for sea-surface targets based on denoising convolutional neural networks(DnCNN) is proposed.
53758	所提方法设计了一个海面目标分类识别模型，该模型通过其中的降噪模块提高信噪比。	The proposed method designed a sea surface target classification and recognition model, which improves the signal-to-noise ratio through the noise reduction module.
53759	首先，分析了HRRP和二维图像的相似特性，将HRRP降噪转变为二维图像降噪。	First, the similar characteristics of HRRP and two-dimensional images are analyzed, and HRRP noise reduction is transformed into two-dimensional image noise reduction.
53760	其次，利用深层次卷积层与批归一化层相结合的结构，提取图像深层次的噪声特征，最后采用残差学习技术，减轻深层次网络的学习负担的同时重构图像进行分类识别。	Secondly, the deep-level convolutional layer and the batch normalization layer are combined to extract the deep-level noise features of the image, and finally the residual learning technology is used to reduce the learning burden of the deep network while reconstructing the image for classification and recognition.
53761	实验结果表明，该模型可以有效提升低信噪比条件下的海面目标分类识别正确率，在不同信噪比条件下其识别性能均优于对比模型，具有良好的识别性能和鲁棒性。	Experimental results show that the model can greatly improve the accuracy of sea-surface target classification and recognition under the condition of low signal-to-noise ratio. What's more, with the features of good recognition performance and robustness, its recognition performance is also better than that of contrast model under different conditions of SNR.
53762	合成孔径雷达(synthetic aperture radar，SAR)图像舰船目标检测紧贴军事和民用需求，为海洋监视提供重要信息支撑。	Synthetic aperture radar(SAR) image ship target detection closely meets military and civilian needs, and provides important information support for marine surveillance.
53763	针对复杂大场景SAR图像，本文设计了一种基于级联网络的舰船目标检测框架，该网络框架主要由D-BiSeNet海陆分割、分块区域筛选和CP-FCOS目标检测三部分组成。	Aiming at complex large-scenes SAR Images, a fast detection framework for ship targets based on a cascade network is proposed in this paper. The detection framework is mainly composed of three parts: D-BiSeNet sea and land segmentation, block area screening and CP-FCOS target detection.
53764	通过改进双边网络(D-BiSeNet)进行SAR图像海陆分割，增强了图像空间位置信息及网络边缘损失，提高了分割性能。	D-BiSeNet is an improved bilateral network(BiSeNet) adapted to the sea and land segmentation of SAR images. It improved the segmentation performance by enhancing image spatial location information and network edge loss.
53765	通过海域面积比参数设定进行分块区域筛选，可以有效选择网络处理图像块，提升算法整体检测效率。	Sea area ratio is set to select network processing image blocks effectively, which can improve the overall detection efficiency of the algorithm.
53766	CP-FCOS网络将Category-Position特征优化模块应用于传统FCOS网络，强化网络特征提取能力，同时改进目标分类和边界框回归方式，提高舰船目标定位效果。	A Category-Position feature optimization module is applied to the traditional FCOS network in CP-FCOS, which can strengthen the feature extraction capabilities of network. Meanwhile, the target classification and boundary box regression methods are redesigned to improve the effect of ship target positioning.