ID 原文 译文
21365 之后，在每个子带信号上，利用基于迭代最小化的稀疏学习方法进行线谱估计，并将各子带上的线谱估计结果进行频域综合滤波以及门限判决等处理。 The sparse learning via iterative minimization method is utilized on each subband to estimate the line spectrum signal. Then, the results of line spectrum estimation on each subband are processed by frequency domain synthesis filtering and threshold decision.
21366 最终得到色噪声背景下的线谱估计结果。 Finally, the line spectrum signal under colored noise background is identified.
21367 理论推导及仿真实验表明所提方法在色噪声背景下具有较好的线谱估计性能。 The oretical derivation and simulation experiments show that the proposed method has better line spectrum estimation performance under colored noise background.
21368 其能够有效地去除色噪声背景，同时保留稀疏重构类线谱估计方法所具有的高频率分辨力等优点。 The colored noise background can be removed, and the advantage of high frequency resolution of sparse reconstruction method is retained.
21369 传统显著性目标检测方法常假设只有单个显著性目标，其效果依赖显著性阈值的选取，并不符合实际应用需求。 Traditional saliency object detection methods, assuming that there is only one salient object, is not conductive to practical application. Their effects are dependent on saliency threshold.
21370 近来利用目标检测方法得到显著性目标检测框成为一种新的解决思路。 Object detection model provides a kind of new solutions.
21371 SSD模型可同时精确检测多个不同尺度的目标对象，但小尺寸目标检测精度不佳。 SSD can accurately detect multi-objects with different scales simultaneously, except for small objects.
21372 为此，该文引入去卷积模块与注意力残差模块，构建了面向多显著性目标检测的DAR-SSD模型。 To overcome this drawback, this paper presents a new multi- saliency objects detection model, DAR-SSD, appending a deconvolution module embedded with an attention residual module.
21373 实验结果表明，DAR-SSD检测精度显著高于SOD模型； Experiments show that DAR-SSD achieves a higher detection accuracy than SOD.
21374 相比原始SSD模型，在小尺度和多显著性目标情形下性能提升明显；相比MDF和DCL等深度学习框架下的方法，也体现了复杂背景情形下的良好检测性能。 Also, it improves detection performance for multi- saliency objects on small scales, compared with original SSD, and it has an advantage over complicated background, compared with MDF and DCL, which also are deep model based methods.