Abstract:

In view of the current deficiencies that the previous saliency detection algorithms are difficult to segment the complete salient region and sharp edge details in complex scenes, a novel feature fusion of saliency detection model is proposed in this paper. The proposed algorithm utilizes full convolution neural network to obtain the initial features of multi-level roughness and combines the feature pyramid structure to analyze its depth. In order to realize the gradual fusion and transmission of features, the progressive structural receptive field module is designed to transform features to different scales of space for optimization. The global attention mechanism is used to eliminate the background noise and establish the long-distance dependence between the saliency pixels, so as to improve the effectiveness of the saliency region, highlight the saliency region, and then obtains the saliency map by learning and fusing the hierarchical features. The comprehensive experiment show that the F-measure index is far beyond the other seven mainstream methods when the absolute error is reduced. The proposed saliency model combines the advantages of full convolution neural network and feature pyramid structure, and combines the gradual structure receptive field and global attention mechanism designed in this study to make the saliency map closer to the truth map.

Key words: saliency detection, fully convolutional networks, feature pyramid, progressive structural receptive field, global attention, F-measure index