基于特征排列和空间激活的显著物体检测方法

显著物体检测目前在计算机视觉领域中非常重要，如何处理不同尺度的特征信息成为能否获得优秀预测结果的关键。该文有两个主要贡献，一是提出一种用于显著目标检测的特征排列方法，基于自编码结构的卷积神经网络模型，利用尺度表征的概念将特征图进行分组和重排列，以获得一个更加泛化的显著目标检测模型和更加准确的显著目标预测结果；二是在输出部分利用了双重卷积残差和FReLU激活函数，抓取更全面的像素信息，完成空间信息上的激活。利用两种算法的特点融合作用于模型的学习训练。实验结果表明，将该文算法与主流的显著目标检测算法进行比较，在所有评测指标上都达到了最优的效果。

Salient Object Detection via Feature Permutation and Space Activation

Salient object detection occupies an important position in the field of computer vision. How to deal with feature information on different scales becomes the key to obtain excellent prediction results. Two contributions are made in this article. On the one hand, a feature permutation method for salient object detection is proposed. The proposed method is a convolutional neural network based on the self-encoding network structure. It uses the concept of scale representation proposed in this paper to group and permute the multiscale feature maps of different layers in the neural network. So the proposed method obtains a more generalized salient object detection model and a more accurate prediction results about salient object detection. On the other hand, the proposed method adopts the double-conv residual and FReLU activation for the output of the model, so that more complete pixel information could be obtained, and the spatial information is also activated as well. The characteristics of the two algorithms are fused to act on the learning and training of the model. Finally, the proposed algorithm is compared with the mainstream salient object detection algorithms, and the experimental results show that the proposed algorithm obtains the best results from all.