为了解决图像显著性检测中传统方法特征学习不全面、复杂场景下显著区域凸出不明显的问题，提出了一种基于多级深度特征和随机游走的显著性检测算法。首先，利用全卷积神经网络，结合深层和浅层卷积特征信息对图像进行多级卷积深度特征提取; 然后，对图像进行超像素分割，将提取的深度卷积特征分配给相应的超像素，构建特征矩阵; 最后，通过正则化随机游走排序模型生成最终的显著图。在 ECSSD 和 DUT-OMRON 数据库上的实验结果表明，与 6 种具有代表性的显著性检测算法相比，文中算法的准确性和 F 值具有一定的优势。

In order to solve the problems of incomplete feature learning and unobvious salient regions in complex scenes for the traditional saliency detection methods，a saliency detection algorithm based on multi-level deep fea-tures and random walk was proposed． Firstly，the fully convolutional networks (FCN) was used to perform multi-level convolution deep feature extraction on the image by combining the deep and shallow feature information． Se-condly，superpixels segmentation on the image was carried out and the extracted deep convolution features were assigned to the corresponding superpixels to construct a feature matrix． Finally，the final saliency map was generated by regularizing the random walk ranking model． The experimental results on the ECSSD and DUT-OMRON databases show that the proposed method has certain advantages in accuracy and F value，compared with the six representa-tive saliency detection algorithms．