多尺度特征提取和多级别特征融合的显著性目标检测方法

显著性目标检测已经被广泛应用到图像检索、图像分割、行人重识别等领域.目前主流的显著性目标检测方法通常采用短连接加权的方式融合多级别特征信息,这种方式无法精准有效地控制信息流的传递.而且,现有的检测方法通常采用单一的特征检测,导致显著性目标区域与背景的边界不连续、易模糊.因此,本文提出一种多尺度特征提取和多级别特征融合的...

Salient Object Detection Based on Multi-scale Feature Extraction and Multi-level Feature Fusion

Current mainstream detection methods fuse multi-level feature information through short connection to add feature maps, which cannot accurately and effectively control the information. In addition, existing salient detection methods usually use single feature detection, which results in discontinuous and fuzzy boundary between the saliency object region and the background. A new salient object detection method based on multi-scale feature extraction and multi-level feature fusion is proposed in this paper. Firstly, we obtain multi-scale context information by using the dilated convolution of different expansion rates to make up for the deficiencies caused by single feature detection. Secondly, a multi-level feature fusion module is designed, which fuses low-level feature, high-level feature and global context feature information for different distribution characteristics of them. It can not only restrain the transmission of noise, but also restore the spatial detail structure information of the saliency object effectively. Experiments on five public datasets show that compared with other thirteen mainstream detection methods, the saliency map predicted by our method has better continuity of edge contours and clearer details of spatial structure details. Moreover, our method is superior to other methods in terms of F-measure, mean absolute error (MAE), structure measure (S-measure), precision-recall (PR) curves and F-score curves.